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1
Yang, Dan, Kai Mu, Hui Yang, Mingliang Luo, Wei Lv, Bin Zhang, Hui Liu, and Zhicheng Wang. "A Study on Prediction Model of Gully Volume Based on Morphological Features in the JINSHA Dry-Hot Valley Region of Southwest China." ISPRS International Journal of Geo-Information 10, no. 5 (May 5, 2021): 300. http://dx.doi.org/10.3390/ijgi10050300.
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Gully erosion is well-developed in the Jinsha dry-hot valley region, which has caused serious soil losses. Gully volume is regarded as an effective indicator that can reflect the development intensity of gully erosion, and the evolutionary processes of gullies can be predicted based on the dynamic variation in gully volume. Establishing an effective prediction model of gully volume is essential to determine gully volume accurately and conveniently. Therefore, in this work, an empirical prediction model of gully volume was constructed and verified based on detailed morphological features acquired by elaborate field investigations and measurements in 134 gullies. The results showed the mean value of gully length, width, depth, cross-section area, volume, and vertical gradient decreased with the weakness of the activity degree of the gully, although the decrease in processes of these parameters had some differences. Moreover, a series of empirical prediction models of gully volume was constructed, and gully length was demonstrated to be a better predictor than other morphological features. Lastly, the effectiveness test showed the model of V = aL^b was the most effective in predicting gully volume among the different models established in this study. Our results provide a useful approach to predict gully volume in dry-hot valley regions.
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Hughes, Andrew O., and Ian P. Prosser. "Gully erosion prediction across a large region: Murray - Darling Basin, Australia." Soil Research 50, no. 4 (2012): 267. http://dx.doi.org/10.1071/sr12025.
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Gully erosion is a significant process for delivering sediment to streams, and can be the dominant erosion process in some regions. As with other forms of erosion, we need methods to predict the extent and patterns of gully erosion across large areas. Such methods also improve our understanding of the environmental controls on gully erosion. Here, patterns of gully density are predicted across the 1 × 106 km2 Murray–Darling Basin in Australia, using aerial photograph mapping of gullies across part of the Basin and a multivariate statistical model of a range of environmental factors. Across the Basin, at a 10-km grid resolution, gully density is predicted to vary from 0 to 1.2 km km–2, with 22% of the Basin having a gully density >0.1 km km–2 and 3% a density >0.5 km km–2. The model is reasonably successful at predicting the variations in mapped gully density compared with similar attempts to predict erosion processes at this scale. Hillslope gradient and mean annual rainfall are the most important single factors across the region. The predicted mean gully density across the Basin is 0.08 km km–2 and gullies contribute up to 27 × 106 t year–1 of sediment to the river network. This is more than the amount that has been estimated from the combined contribution of hillslope (14 × 106 t year–1) and riverbank (8.6 × 106 t year–1) erosion by other studies within the Basin.
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Arabameri, Alireza, Wei Chen, Thomas Blaschke, John P. Tiefenbacher, Biswajeet Pradhan, and Dieu Tien Bui. "Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran." Water 12, no. 1 (December 19, 2019): 16. http://dx.doi.org/10.3390/w12010016.
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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach—bagging-based alternating decision-tree classifier (bagging-ADTree)—and use it to model a landscape’s susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model’s goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models’ analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model’s results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.
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Amare, Selamawit, Eddy Langendoen, Saskia Keesstra, Martine Ploeg, Habtamu Gelagay, Hanibal Lemma, and Sjoerd Zee. "Susceptibility to Gully Erosion: Applying Random Forest (RF) and Frequency Ratio (FR) Approaches to a Small Catchment in Ethiopia." Water 13, no. 2 (January 18, 2021): 216. http://dx.doi.org/10.3390/w13020216.
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Soil erosion by gullies in Ethiopia is causing environmental and socioeconomic problems. A sound soil and water management plan requires accurately predicted gully erosion hotspot areas. Hence, this study develops a gully erosion susceptibility map (GESM) using frequency ratio (FR) and random forest (RF) algorithms. A total of 56 gullies were surveyed, and their extents were derived by digitizing Google Earth imagery. Literature review and a multicollinearity test resulted in 14 environmental variables for the final analysis. Model prediction potential was evaluated using the area under the curve (AUC) method. Results showed that the best prediction accuracy using the FR and RF models was obtained by using the top four most important gully predictor factors: drainage density, elevation, land use, and groundwater table. The notion that the groundwater table is one of the most important gully predictor factors in Ethiopia is a novel and significant quantifiable finding and is critical to the design of effective watershed management plans. Results from separate variable importance analyses showed land cover for Nitisols and drainage density for Vertisols as leading factors determining gully locations. Factors such as texture, stream power index, convergence index, slope length, and plan and profile curvatures were found to have little significance for gully formation in the studied catchment.
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Javidan, Kavian, Pourghasemi, Conoscenti, and Jafarian. "Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios." Water 11, no. 11 (November 6, 2019): 2319. http://dx.doi.org/10.3390/w11112319.
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Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with five, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features.
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Jiang, Chengcheng, Wen Fan, Ningyu Yu, and Yalin Nan. "A New Method to Predict Gully Head Erosion in the Loess Plateau of China Based on SBAS-InSAR." Remote Sensing 13, no. 3 (January 26, 2021): 421. http://dx.doi.org/10.3390/rs13030421.
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Gully head erosion causes serious land degradation in semiarid regions. The existing studies on gully head erosion are mainly based on measuring the gully volume in small-scale catchments, which is a labor-intensive and time-consuming approach. Therefore, it is necessary to explore an accurate method quantitatively over large areas and long periods. The objective of this study was to develop a model to assess gully head erosion in the Loess Plateau of China using a method based on the SBAS-InSAR technique. The gully heads were extracted from the digital elevation model and validated by field investigation and aerial images. The surface deformation was estimated with SBAS-InSAR and 22 descending ALOS PALSAR datasets from 2007 to 2011. A gully head erosion model was developed; this model can incorporate terrain factors and soil types, as well as provides erosion rate predictions consistent with the SBAS-InSAR measurements (R2 = 0.889). The results show that gully head erosion significantly depends on the slope angle above the gully head, slope length, topographic wetness index, and catchment area. The relationship between these factors and the gully head erosion rate is a power function, and the average rate of gully head erosion is 7.5 m3/m2/year, indicating the high erosional vulnerability of the area. The accuracy of the model can be further improved by considering other factors, such as the stream power factor, curvature, and slope aspect. This study indicates that the erosion rate of gully heads is almost unaffected by soil type in the research area. An advantage of this model is that the gully head area and surface deformation can be easily extracted and measured from satellite images, which is effective for assessing gully head erosion at a large scale in combination with SBAS-InSAR results and terrain attributes.
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Chowdhuri, Indrajit, Subodh Chandra Pal, Alireza Arabameri, Asish Saha, Rabin Chakrabortty, Thomas Blaschke, Biswajeet Pradhan, and Shahab S. Band. "Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment." Remote Sensing 12, no. 21 (November 4, 2020): 3620. http://dx.doi.org/10.3390/rs12213620.
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The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.
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Arabameri, Alireza, Thomas Blaschke, Biswajeet Pradhan, Hamid Reza Pourghasemi, John P. Tiefenbacher, and Dieu Tien Bui. "Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study." Sensors 20, no. 2 (January 7, 2020): 335. http://dx.doi.org/10.3390/s20020335.
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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.
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Azedou, Ali, Said Lahssini, Abdellatif Khattabi, Modeste Meliho, and Nabil Rifai. "A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)." Sustainability 13, no. 2 (January 12, 2021): 682. http://dx.doi.org/10.3390/su13020682.
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Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods).
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Pal, Subodh Chandra, Alireza Arabameri, Thomas Blaschke, Indrajit Chowdhuri, Asish Saha, Rabin Chakrabortty, Saro Lee, and Shahab S. Band. "Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility." Remote Sensing 12, no. 22 (November 10, 2020): 3675. http://dx.doi.org/10.3390/rs12223675.
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Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harmful consequences. The principle goal of the present research study is to develop GES maps for the Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using a machine learning algorithm (MLA) of boosted regression tree (BRT), bagging and the ensemble of BRT-bagging with K-fold cross validation (CV) resampling techniques. The combination of the aforementioned MLAs with resampling approaches is state-of-the-art soft computing, not often used in GES evaluation. In further progress of our research work, here we used a total of 20 gully erosion conditioning factors (GECFs) and a total of 199 gully head cut points for modelling GES. The variables’ importance, which is responsible for gully erosion, was determined based on the random forest (RF) algorithm among the several GECFs used in this study. The output result of the model’s performance was validated through a receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) statistical analysis. The predicted result shows that the ensemble of BRT-bagging is the most well fitted for GES where AUC value in K-3 fold is 0.972, whereas the value of AUC in sensitivity, specificity, PPV and NPV is 0.94, 0.93, 0.96 and 0.93, respectively, in a training dataset, and followed by the bagging and BRT model. Thus, from the predictive performance of this research study it is concluded that the ensemble of BRT-Bagging can be applied as a new approach for further studies in spatial prediction of GES. The outcome of this work can be helpful to policy makers in implementing remedial measures to minimize damages caused by gully erosion.
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Arabameri, Cerda, Rodrigo-Comino, Pradhan, Sohrabi, Blaschke, and Tien Bui. "Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran)." Remote Sensing 11, no. 21 (November 2, 2019): 2577. http://dx.doi.org/10.3390/rs11212577.
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Gully erosion is considered to be one of the main causes of land degradation in arid and semi-arid territories around the world. In this research, gully erosion susceptibility mapping was carried out in Semnan province (Iran) as a case study in which we tested the efficiency of the index of entropy (IoE), the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and their combination. Remote sensing and geographic information system (GIS) were used to reduce the time and costs needed for rapid assessment of gully erosion. Firstly, a gully erosion inventory map (GEIM) with 206 gully locations was obtained from various sources and randomly divided into two groups: A training dataset (70% of the data) and a validation dataset (30% of the data). Fifteen gully-related conditioning factors (GRCFs) including elevation, slope, aspect, plan curvature, stream power index, topographical wetness index, rainfall, soil type, drainage density, distance to river, distance to road, distance to fault, lithology, land use/land cover, and soil type, were used for modeling. The advanced land observing satellite (ALOS) digital elevation model with a spatial resolution of 30 m was used for the extraction of the above-mentioned topographic factors. The tolerance (TOL) and variance inflation factor (VIF) were also included for checking the multicollinearity among the GRCFs. Based on IoE, we concluded that soil type, lithology, and elevation were the most significant in terms of gully formation. Validation results using the area under the receiver operating characteristic curve (AUROC) showed that IoE (0.941) reached a higher prediction accuracy than VIKOR (0.857) and VIKOR-IoE (0.868). Based on our results, the combination of statistical (IoE) models along with remote sensing and GIS can convert the multi-criteria decision-making (MCDM) models into efficient and powerful tools for gully erosion prediction. We strongly suggest that decision-makers and managers should use these kinds of results to develop more consistent solutions to achieve sustainable development on degraded lands such as in the Semnan province.
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Barber, Michael, and Robert Mahler. "Ephemeral gully erosion from agricultural regions in the Pacific Northwest, USA." Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 42, no. 1 (January 1, 2010): 23–29. http://dx.doi.org/10.2478/v10060-008-0061-y.
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Ephemeral gully erosion from agricultural regions in the Pacific Northwest, USA Soil erosion continues to be problematic financially and environmentally with the USEPA ranking sediment as one of the top ten pollutants of concern in the USA. One aspect of erosion often overlooked is the role of ephemeral gullies in terms of quantity of sediment produced and amount exported to nearby waterways. Current physically-based and empirical models are inadequate for predicting this type of erosion particularly at the watershed scale. A new methodology for predicting the quantity and location of sediment delivery was developed and tested via a case study. Aerial ephemeral gully erosion rates varied from 33.6 mton/km2 (0.15 U.S. tons/acre) in the Big Bear Creek basin to 88.4 mton/km2 (0.39 U.S. tons/acre) in the Middle Potlatch Creek basin representing 2.3 to 7.7% of the total surface sediment load. This information was used to develop a predictive Erosion Potential Index (EPI) that uses LANDSAT aerial imagery combined with readily available soils information and a digital elevation model to identify the most probably locations of ephemeral gully development. High resolution aerial imagery was used to quantify actual ephemeral gully locations which were then compared to the EPI predicted locations to verify the procedure. High resolution aerial imagery was also used to quantify the amounts of soil erosion from ephemeral gullies in basins of the Potlatch River system.
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Arabameri, Alireza, Wei Chen, Luigi Lombardo, Thomas Blaschke, and Dieu Tien Bui. "Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment." Remote Sensing 12, no. 1 (January 1, 2020): 140. http://dx.doi.org/10.3390/rs12010140.
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Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher’s linear discriminant analysis (FLDA), logistic model tree (LMT) and naïve Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data.
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Romanescu, G., V. Cotiuga, A. Asandulesei, and C. Stoleriu. "Use of the 3-D scanner in mapping and monitoring the dynamic degradation of soils: case study of the Cucuteni-Baiceni Gully on the Moldavian Plateau (Romania)." Hydrology and Earth System Sciences 16, no. 3 (March 22, 2012): 953–66. http://dx.doi.org/10.5194/hess-16-953-2012.
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Abstract. The 3-D scanner, a rapid and precise means of monitoring the dynamics of erosive processes, was first used nationally (Romania) as a new technique of cartography and monitoring the dynamics of soil degradation processes in the Moldavian Plateau. Three sets of measurements took place: in 2008, in 2009 and in 2010, at intervals of exactly one year for the first and six months for the second part. Qualitative and quantitative differences were highlighted. The data obtained were corroborated with precipitation in the area studied. The 3-D scanner has a measurement accuracy of 6 mm. The map highlights the dynamics of gullies developed and may form the basis for the prediction of soil degradation phenomena. The dynamics of the gully and the type of land use show that the phenomenon of erosion of the Moldova Plateau will continue to accelerate. In this case, the gully attacked and destroyed an archaeological site of national importance. The rate of advance of the Cucuteni-Baiceni gully is extremely high (10 m/1.6 years). There are no measures at all to reduce or fight the process of the gully advance. Maximum erosion occurred at the beginning of spring after a winter rich in rainfall, which made the terrain subject to the process of subsidence.
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Romanescu, G., B. Venedict, V. Cotiuga, and A. Asandulesei. "Use of the 3-D scanner in mapping and monitoring the dynamic degradation of soils. Case study of the Cucuteni-Baiceni Gully on the Moldavian Plateau (Romania)." Hydrology and Earth System Sciences Discussions 8, no. 4 (July 14, 2011): 6907–37. http://dx.doi.org/10.5194/hessd-8-6907-2011.
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Abstract. The 3-D Scanner, a rapid and precise means of monitoring the dynamics of erosive processes, was used, first of all nationally (Romania), as a new technique of cartography and monitoring the dynamics of soil degradation processes in the Moldavian Plateau. Three sets of measurements took place: in 2008, in 2009 and in 2010, at intervals of exactly one year for the first and six months for the second part. Qualitative and quantitative differences were highlighted. The data obtained were corroborated with precipitation in the area studied. The 3-D scanner has a measurement accuracy of 6 mm. The map highlights the dynamics of gullies developed and may form the basis for the prediction of soil degradation phenomena. The dynamics of the gully and the type of land use show that the phenomenon of erosion of the Moldova Plateau will continue to accelerate. In this case the gully attacked and destroyed an archaeological site of national importance. The rate of advance of the Cucuteni-Baiceni gully is extremely high (10 m/1.6 yr). There are no measures at all to reduce or fight the process of the gully advance. Maximum erosion occurred at the beginning of spring after a winter rich in rainfall, which made the terrain subject to the process of subsidence.
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Arabameri, Alireza, Biswajeet Pradhan, Hamid Reza Pourghasemi, Khalil Rezaei, and Norman Kerle. "Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms." Applied Sciences 8, no. 8 (August 14, 2018): 1369. http://dx.doi.org/10.3390/app8081369.
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Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.
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Nhu, Viet-Ha, Saeid Janizadeh, Mohammadtaghi Avand, Wei Chen, Mohsen Farzin, Ebrahim Omidvar, Ataollah Shirzadi, et al. "GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models." Applied Sciences 10, no. 6 (March 17, 2020): 2039. http://dx.doi.org/10.3390/app10062039.
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Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.
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Dabney, S. M., D. A. N. Vieira, and D. C. Yoder. "Predicting ephemeral gully erosion with RUSLER and EphGEE." Proceedings of the International Association of Hydrological Sciences 367 (March 3, 2015): 72–79. http://dx.doi.org/10.5194/piahs-367-72-2015.
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Abstract. Ephemeral gully erosion is not included in predictions made with the Revised Universal Soil Loss Equation, version 2 (RUSLE2). A new distributed application called RUSLER (RUSLE2-Raster) predicts distributed soil loss and its output can be linked with the new Ephemeral Gully Erosion Estimator (EphGEE). These models were applied to a 6.3 ha research watershed near Treynor, Iowa, USA, where runoff and sediment yield were measured from 1975 to 1991. Using a 3-m raster DEM, results indicate that ephemeral gully erosion contributed about one-third of the amount of sheet and rill erosion, and that considerable deposition of sediment originating from both sources occurred within the grassed waterway. For ambient conditions, predicted annual average watershed sediment yield was 17.5 Mg ha−1 year−1, 20% greater than the measured value of 14.6 Mg ha−1 year−1.
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Samani, Aliakbar Nazari, Qiuwen Chen, Shahram Khalighi, Robert James Wasson, and Mohammad Reza Rahdari. "Assessment of land use impact on hydraulic threshold conditions for gully head cut initiation." Hydrology and Earth System Sciences 20, no. 7 (July 28, 2016): 3005–12. http://dx.doi.org/10.5194/hess-20-3005-2016.
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Abstract. A gully as an accelerated erosion process is responsible for land degradation under various environmental conditions and has been known as a threshold phenomenon. Although the effects of gullying processes have been well documented, few soil erosion models have taken into account the threshold condition necessary for gully development. This research was devoted to determining the effects of land use change on hydraulic threshold condition and stream power of water flow through an in situ experimental flume (15 m × 0.4 m). Results indicated that head cut initiation and detachment rates showed a better correlation to stream power indices than shear stress (τcr). The threshold unit stream power value (ωu) for head cut initiation in rangeland, abandoned land, and dry farming land was 0.0276, 0.0149, and 4.5 × 10−5 m s−1, respectively. Moreover, the micro-relief condition of soil surface and surface vegetation affected the flow regime of discharge and velocity. It is seen that the composite hydraulic criteria of Froude number (Fr) and discharge (Q) can clearly discriminate the land uses' threshold. In fact, the remarkable decrease of τcr in dry farming was related to the effect of tillage practice on soil susceptibility and aggregate strength. The findings indicated that using the unit steam power index instead of critical shear stress could increase the models' precision for prediction of head cut development. Compared to the Ephemeral Gully Erosion Model (EGEM) equation for critical shear stress, it is important to point out that for modelling of gully erosion, using single soil attributes can lead to an inaccurate estimation for τcr. In addition, based on the findings of this research, the use of threshold values of τcr = 35 dyne cm−2 and ωu = 0.4 cm s−1 in physically based soil erosion models is susceptible to high uncertainty when assessing gully erosion.
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Crouch, RJ. "The relationship of gully sidewall shape to sediment production." Soil Research 25, no. 4 (1987): 531. http://dx.doi.org/10.1071/sr9870531.
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Reports of gully side erosion describe a range of side forms. The lack of information on the relative significance of the various forms in terms of sediment production hinders the identification of the major sediment sources within gullies. Observations of gully sides in a catchment in central New South Wales showed a range of side forms being eroded at significantly different rates. Side classification and measurement by survey and erosion pins showed that vertical sides, subject to undercutting, had the highest erosion rate (75 mm yr-l) followed by vertical fluted walls (37 mm yr-l). These different rates are critical in predicting present and future rates of erosion and identifying sediment sources within gully systems.
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van Zyl, A. J. "A knowledge gap analysis on multi-scale predictive ability for agriculturally derived sediments under South African conditions." Water Science and Technology 55, no. 3 (February 1, 2007): 107–14. http://dx.doi.org/10.2166/wst.2007.078.
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Agriculture has been implicated as a major source of sediments in South Africa. The aim of the knowledge gap analysis was to understand the production and delivery components of agriculturally derived sediments under South African conditions and to assess the predictive ability to address the fate of these sediments from field to catchment scales. An overview is given of important erosion processes and erosion modelling applied in South Africa at the field and catchment scale. A limitation of the sediment models is that gully erosion is not simulated; therefore, the models should be complemented with gully erosion predictions if gullies are an important sediment source. Field-scale models inadequately predict sediment production localised at hydrologically sensitive areas as a result of saturation excess flow and/or throughflow. The discussion on erosion modelling reveals that more complex models have had limited application in South Africa because they require large and detailed data sets, and may have parameters that are difficult to measure or to estimate. A modelling framework is discussed which allows linking of sediment models requiring readily available data, gully erosion models/maps and the use of other techniques to assess the fate of agriculturally derived sediments from field to catchment scale.
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Rouhani, Hamed, Aboalhasan Fathabadi, and Jantiene Baartman. "A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping." Progress in Physical Geography: Earth and Environment 45, no. 4 (January 20, 2021): 580–99. http://dx.doi.org/10.1177/0309133320979897.
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Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The results showed that the elevation, distance to fault, slope and the index of connectivity were the most important causal factors affecting the successful prediction of gully occurrence. Comparison of maximum True Skill Statistic values showed that increased model sophistication did not necessarily result in a higher level of model performance. In terms of performance, k-NN was superior to the SVM and LR methods. This method can be effectively used for gully erosion susceptibility (GES) zonation in the study area, which is very important to support spatial planning to initiate designing mitigation strategies and conservation needs over a large area, or to plan additional conservation efforts and relocate soil conservation plans. In conclusion, our findings indicate that by incorporating the proposed hybrid intelligent approach, the number of relevant factors for GES mapping was reduced, while classification accuracy was increased.
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Langendoen, E. J., R. R. Wells, M. E. Ursic, D. A. N. Vieira, and S. M. Dabney. "Evaluating sediment transport capacity relationships for use in ephemeral gully erosion models." Proceedings of the International Association of Hydrological Sciences 367 (March 3, 2015): 128–33. http://dx.doi.org/10.5194/piahs-367-128-2015.
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Abstract. On cropland, ephemeral gully erosion in the USA may contribute up to 40% of the sediment delivered to the edge of the field. Well-tested, physically- and process-based tools for field and watershed scale prediction of gully erosion are lacking due to the fact that the complex nature of migrating headcuts is poorly understood. Understanding sediment transport capacity downstream of migrating headcuts is essential, as sediment deposition often leads to temporary storage that controls downstream water elevation, which in turn affects the rate of headcut migration. Current process-based gully erosion prediction technology used by the Agricultural Research Service (ARS) is based on characterizing the headcut migration rate, which requires the deposition depth as input to the model. Alternatively, the deposition depth can be calculated if downstream sediment transport capacity can be predicted. Data collected at the ARS-National Sedimentation Laboratory were used to test existing sediment transport relationships for the five sediment size classes (clay, silt, sand, small aggregates, large aggregates) typically used in ARS soil erosion models. The results show that the transport rate can be satisfactorily predicted for sand and large aggregate size fractions using common transport relationships based on unit stream power theory. The fractional content of the sand and large aggregate size classes can be computed using standard relationships, which are based on soil texture, previously developed by ARS. The transport of clays, silts and small aggregates is detachment limited and must therefore be computed using improved soil detachment relationships for ephemeral gullies.
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Gündoğan, R., V. Alma, T. Dindaroğlu, H. Günal, T. Yakupoğlu, T. Susam, and K. Saltalı. "MONITORING AND ESTIMATION OF SOIL LOSSES FROM EPHEMERAL GULLY EROSION IN MEDITERRANEAN REGION USING LOW ALTITUDE UNMANNED AERIAL VEHICLES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W6 (November 13, 2017): 59–61. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w6-59-2017.
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Calculation of gullies by remote sensing images obtained from satellite or aerial platforms is often not possible because gullies in agricultural fields, defined as the temporary gullies are filled in a very short time with tillage operations. Therefore, fast and accurate estimation of sediment loss with the temporary gully erosion is of great importance. <br><br> In this study, it is aimed to monitor and calculate soil losses caused by the gully erosion that occurs in agricultural areas with low altitude unmanned aerial vehicles. According to the calculation with Pix4D, gully volume was estimated to be 10.41&thinsp;m<sup>3</sup> and total loss of soil was estimated to be 14.47 Mg. The RMSE value of estimations was found to be 0.89. The results indicated that unmanned aerial vehicles could be used in predicting temporary gully erosion and losses of soil.
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Arabameri, Alireza, Artemi Cerda, and John P. Tiefenbacher. "Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence." Water 11, no. 6 (May 29, 2019): 1129. http://dx.doi.org/10.3390/w11061129.
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Gully erosion is an environmental problem in arid and semi-arid areas. Gullies threaten the soil and water resources and cause off- and on-site problems. In this research, a new hybrid model combines the index-of-entropy (IoE) model with the weight-of-evidence (WoE) model. Remote sensing and GIS techniques are used to map gully-erosion susceptibility in the watershed of the Bastam district of Semnan Province in northern Iran. The performance of the hybrid model is assessed by comparing the results with from models that use only IoE or WoE. Three hundred and three gullies were mapped in the study area and were randomly classified into two groups for training (70% or 212 gullies) and validation (30% or 91 gullies). Eighteen topographical, hydrological, geological, and environmental conditioning factors were considered in the modeling process. Prediction-rate curves (PRCs) and success-rate curves (SRCs) were used for validation. Results from the IoE model indicate that drainage density, slope, and rainfall factors are the most important factors promoting gullying in the study area. Validation results indicate that the ensemble model performed better than either the IoE or WoE models. The hybrid model predicted that 38.02 percent of the study area has either high or very high susceptible to gullying. Given the high accuracy of the novel hybrid model, this scientific methodology may be very useful for land use management decisions and for land use planning in gully-prone regions. Our research contributes to achieve Land Degradation Neutrality as will help to design remediation programs to control non-sustainable soil erosion rates.
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Wu, Shangwei, Dongming Wu, Xiaofei Jing, Xuanyi Chen, Yijun Wang, and Luhua Ye. "Rainfall Erosion Predictions for Artificial High-Filled Embankment with Reinforcement." Advances in Materials Science and Engineering 2021 (September 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/3648105.
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In recent years, rainstorm disasters caused by global warming have frequently occurred in China. It has caused serious damage to artificial high embankments. In this paper, the influence of rainfall intensity, slope, and reinforced layers on the erosion and destruction of the artificial high embankment is deeply analyzed. Through the model test, the rainfall erosion prediction model is established. The results show that (1) the gully width, depth, and erosion amount increased with the increase in rainfall intensity and slope and decreased with the increase in reinforcement layers; (2) the final ditch shape of the embankment is influenced by steel bars; and (3) according to the model test data, the mathematical model of dike scouring is established. Rainfall intensity and the coupling between slope and reinforced layers are considered in the model. It can be used for predicting erosion during rainfall.
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Arabameri, Alireza, Subodh Chandra Pal, Romulus Costache, Asish Saha, Fatemeh Rezaie, Amir Seyed Danesh, Biswajeet Pradhan, Saro Lee, and Nhat-Duc Hoang. "Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms." Geomatics, Natural Hazards and Risk 12, no. 1 (January 1, 2021): 469–98. http://dx.doi.org/10.1080/19475705.2021.1880977.
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Hancock, G. R., K. G. Evans, G. R. Willgoose, D. R. Moliere, M. J. Saynor, and R. J. Loch. "Medium-term erosion simulation of an abandoned mine site using the SIBERIA landscape evolution model." Soil Research 38, no. 2 (2000): 249. http://dx.doi.org/10.1071/sr99035.
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This study forms part of a collaborative project designed to validate the long-term erosion predictions of the SIBERIA landform evolution model on rehabilitated mine sites. The SIBERIA catchment evolution model can simulate the evolution of landforms resulting from runoff and erosion over many years. SIBERIA needs to be calibrated before evaluating whether it correctly models the observed evolution of rehabilitated mine landforms. A field study to collect data to calibrate SIBERIA was conducted at the abandoned Scinto 6 uranium mine located in the Kakadu Region, Northern Territory, Australia. The data were used to fit parameter values to a sediment loss model and a rainfall–runoff model. The derived runoff and erosion model parameter values were used in SIBERIA to simulate 50 years of erosion by concentrated flow on the batters of the abandoned site. The SIBERIA runs correctly simulated the geomorphic development of the gullies on the man-made batters of the waste rock dump. The observed gully position, depth, volume, and morphology on the waste rock dump were quantitatively compared with the SIBERIA simulations. The close similarities between the observed and simulated gully features indicate that SIBERIA can accurately predict the rate of gully development on a man-made post-mining landscape over periods of up to 50 years. SIBERIA is an appropriate model for assessment of erosional stability of rehabilitated mine sites over time spans of around 50 years.
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Band, Shahab S., Saeid Janizadeh, Subodh Chandra Pal, Asish Saha, Rabin Chakrabortty, Manouchehr Shokri, and Amirhosein Mosavi. "Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility." Sensors 20, no. 19 (September 30, 2020): 5609. http://dx.doi.org/10.3390/s20195609.
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This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.
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Tien Bui, Dieu, Ataollah Shirzadi, Himan Shahabi, Kamran Chapi, Ebrahim Omidavr, Binh Thai Pham, Dawood Talebpour Asl, et al. "A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)." Sensors 19, no. 11 (May 29, 2019): 2444. http://dx.doi.org/10.3390/s19112444.
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In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
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Chen, Yixian, Juying Jiao, Yanhong Wei, Hengkang Zhao, Weijie Yu, Binting Cao, Haiyan Xu, Fangchen Yan, Duoyang Wu, and Hang Li. "Accuracy Assessment of the Planar Morphology of Valley Bank Gullies Extracted with High Resolution Remote Sensing Imagery on the Loess Plateau, China." International Journal of Environmental Research and Public Health 16, no. 3 (January 28, 2019): 369. http://dx.doi.org/10.3390/ijerph16030369.
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Gully erosion is a serious environmental problem worldwide, causing soil loss, land degradation, silting up of reservoirs and even catastrophic flooding. Mapping gully features from remote sensing imagery is crucial for assisting in understanding gully erosion mechanisms, predicting its development processes and assessing its environmental and socio-economic effects over large areas, especially under the increasing global climate extremes and intensive human activities. However, the potential of using increasingly available high-resolution remote sensing imagery to detect and delineate gullies has been less evaluated. Hence, 130 gullies occurred along a transect were selected from a typical watershed in the hilly and gully region of the Chinese Loess Plateau, and visually interpreted from a Pleiades-1B satellite image (panchromatic-sharpened image at 0.5 m resolution fused with 2.0 m multi-spectral bands). The interpreted gullies were compared with their measured data obtained in the field using a differential global positioning system (GPS). Results showed that gullies could generally be accurately interpreted from the image, with an average relative error of gully area and gully perimeter being 11.1% and 8.9%, respectively, and 74.2% and 82.3% of the relative errors for gully area and gully perimeter were within 15%. But involving field measurements of gullies in present imagery-based gully studies is still recommended. To judge whether gullies were mapped accurately further, a standard adopting one-pixel tolerance along the mapped gully edges was proposed and proved to be practical. Correlation analysis indicated that larger gullies could be interpreted more accurately but increasing gully shape complexity would decrease interpreting accuracy. Overall lower vegetation coverage in winter due to the withering and falling of vegetation rarely affected gully interpreting. Furthermore, gully detectability on remote sensing imagery in this region was lower than the other places of the world, due to the overall broken topography in the Loess Plateau, thus images with higher resolution than normally perceived are needed when mapping erosion features here. Taking these influencing factors (gully dimension and shape complexity, vegetation coverage, topography) into account will be favorable to select appropriate imagery and gullies (as study objects) in future imagery-based gully studies. Finally, two linear regression models were built to correct gully area (Aip, m2) and gully perimeter (Pip, m) visually extracted, by connecting them with the measured area (Ams, m2) and perimeter (Pms, m). The correction models were Ams=1.021Aip+0.139 and Pms=0.949Pip+ 0.722, respectively. These models could be helpful for improving the accuracy of interpreting results, and further accurately estimating gully development and developing more effective automated gully extraction methods on the Loess Plateau of China.
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Dumbrovsky, Miroslav, Karel Drbal, Veronika Sobotková, and Jana Uhrová. "An approach to identifying and evaluating the potential formation of ephemeral gullies in the conditions of the Czech Republic." Soil and Water Research 15, No. 1 (December 9, 2019): 38–46. http://dx.doi.org/10.17221/231/2018-swr.
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Soil erosion, including ephemeral gully erosion, is a serious degradation process in the Czech Republic. It currently threatens more than half of the agricultural acreage through negative changes in the whole complex of soil properties. The unfavourable consequences of surface runoff are seen in the erosion processes degrading agricultural soils. The South Moravia Region was selected as the case study area – mainly for its natural conditions and high soil degradation risk . A set of data, collected from 2012 to 2017 in a maize-growing area, especially on deep loess soils in the South Moravia Region, was used to analyse the morphological characteristics of the ephemeral gullies (EGs). The relationship was confirmed between the ephemeral gully (EG) length and the size of its contributing drainage area in accordance with studies conducted in other countries. It is also important that the closest relationship was confirmed between the length of the gully and its calculated volume. Dependence was sought on the data of 51 cases of the detailed, measured and evaluated EGs. These results will become the basis for finding a predictive relationship and the quantification of EG erosion. Locating EGs and predicting their length is crucial for estimating the sediment load and planning conservation strategies. The aim of this paper is to contribute to the understanding of this issue, i.e., define and verify the basic crucial causal factors and propose guidelines for locating the potential EG occurrence and predicting the sediment load. A research effort to better understand the EG mechanism and causal factors over a wide range of watershed conditions is fundamental to the establishment of basic rules for the adoption of optimal conservation strategies.
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Vieira, D. A. N., S. M. Dabney, and D. C. Yoder. "Distributed soil loss estimation system including ephemeral gully development and tillage erosion." Proceedings of the International Association of Hydrological Sciences 367 (March 3, 2015): 80–86. http://dx.doi.org/10.5194/piahs-367-80-2015.
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Abstract. A new modelling system is being developed to provide spatially-distributed runoff and soil erosion predictions for conservation planning that integrates the 2D grid-based variant of the Revised Universal Soil Loss Equation, version 2 model (RUSLER), the Ephemeral Gully Erosion Estimator (EphGEE), and the Tillage Erosion and Landscape Evolution Model (TELEM). Digital representations of the area of interest (field, farm or entire watershed) are created using high-resolution topography and data retrieved from established databases of soil properties, climate, and agricultural operations. The system utilizes a library of processing tools (LibRaster) to deduce surface drainage from topography, determine the location of potential ephemeral gullies, and subdivide the study area into catchments for calculations of runoff and sheet-and-rill erosion using RUSLER. EphGEE computes gully evolution based on local soil erodibility and flow and sediment transport conditions. Annual tillage-induced morphological changes are computed separately by TELEM.
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Bagarello, Vincenzo, Vito Ferro, and Dennis Flanagan. "Predicting plot soil loss by empirical and process-oriented approaches. A review." Journal of Agricultural Engineering 49, no. 1 (April 5, 2018): 1–18. http://dx.doi.org/10.4081/jae.2018.710.
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Soil erosion directly affects the quality of the soil, its agricultural productivity and its biological diversity. Many mathematical models have been developed to estimate plot soil erosion at different temporal scales. At present, empirical soil loss equations and process-oriented models are considered as constituting a complementary suite of models to be chosen to meet the specific user need. In this paper, the Universal Soil Loss Equation and its revised versions are first reviewed. Selected methodologies developed to estimate the factors of the model with the aim to improve the soil loss estimate are described. Then the Water Erosion Prediction Project which represents a process-oriented technology for soil erosion prediction at different spatial scales, is presented. The available criteria to discriminate between acceptable and unacceptable soil loss estimates are subsequently introduced. Finally, some research needs, concerning tests of both empirical and process-oriented models, estimates of the soil loss of given return periods, reliability of soil loss measurements, measurements of rill and gully erosion, and physical models are delineated.
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Nicu, Ionut. "Is Overgrazing Really Influencing Soil Erosion?" Water 10, no. 8 (August 13, 2018): 1077. http://dx.doi.org/10.3390/w10081077.
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Soil erosion is a serious problem spread over a variety of climatic areas around the world. The main purpose of this paper is to produce gully erosion susceptibility maps using different statistical models, such as frequency ratio (FR) and information value (IV), in a catchment from the northeastern part of Romania, covering a surface of 550 km2. In order to do so, a total number of 677 gullies were identified and randomly divided into training (80%) and validation (20%) datasets. In total, 10 conditioning factors were used to assess the gully susceptibility index (GSI); namely, elevation, precipitations, slope angle, curvature, lithology, drainage density, topographic wetness index, landforms, aspect, and distance from rivers. As a novelty, overgrazing was added as a conditioning factor. The final GSI maps were classified into four susceptibility classes: low, medium, high, and very high. In order to evaluate the two models prediction rate, the AUC (area under the curve) method was used. It has been observed that adding overgrazing as a contributing factor in calculating GSI does not considerably change the final output. Better predictability (0.87) and success rate (0.89) curves were obtained with the IV method, which proved to be more robust, unlike FR method, with 0.79 value for both predictability and success rate curves. When using sheepfolds, the value decreases by 0.01 in the case of the FR method, and by 0.02 in the case of the success rate curve for the IV method. However, this does not prove the fact that overgrazing is not influencing or accelerating soil erosion. A multi-temporal analysis of soil erosion is needed; this represents a future working hypothesis.
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Micallef, Aaron, Remus Marchis, Nader Saadatkhah, Potpreecha Pondthai, Mark E. Everett, Anca Avram, Alida Timar-Gabor, et al. "Groundwater erosion of coastal gullies along the Canterbury coast (New Zealand): a rapid and episodic process controlled by rainfall intensity and substrate variability." Earth Surface Dynamics 9, no. 1 (January 8, 2021): 1–18. http://dx.doi.org/10.5194/esurf-9-1-2021.
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Abstract. Gully formation has been associated to groundwater seepage in unconsolidated sand- to gravel-sized sediments. Our understanding of gully evolution by groundwater seepage mostly relies on experiments and numerical simulations, and these rarely take into consideration contrasts in lithology and permeability. In addition, process-based observations and detailed instrumental analyses are rare. As a result, we have a poor understanding of the temporal scale of gully formation by groundwater seepage and the influence of geological heterogeneity on their formation. This is particularly the case for coastal gullies, where the role of groundwater in their formation and evolution has rarely been assessed. We address these knowledge gaps along the Canterbury coast of the South Island (New Zealand) by integrating field observations, luminescence dating, multi-temporal unoccupied aerial vehicle and satellite data, time domain electromagnetic data and slope stability modelling. We show that gully formation is a key process shaping the sandy gravel cliffs of the Canterbury coastline. It is an episodic process associated to groundwater flow that occurs once every 227 d on average, when rainfall intensities exceed 40 mm d−1. The majority of the gullies in a study area southeast (SE) of Ashburton have undergone erosion, predominantly by elongation, during the last 11 years, with the most recent episode occurring 3 years ago. Gullies longer than 200 m are relict features formed by higher groundwater flow and surface erosion > 2 ka ago. Gullies can form at rates of up to 30 m d−1 via two processes, namely the formation of alcoves and tunnels by groundwater seepage, followed by retrogressive slope failure due to undermining and a decrease in shear strength driven by excess pore pressure development. The location of gullies is determined by the occurrence of hydraulically conductive zones, such as relict braided river channels and possibly tunnels, and of sand lenses exposed across sandy gravel cliffs. We also show that the gully planform shape is generally geometrically similar at consecutive stages of evolution. These outcomes will facilitate the reconstruction and prediction of a prevalent erosive process and overlooked geohazard along the Canterbury coastline.
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Micallef, Aaron, Remus Marchis, Nader Saadatkhah, Potpreecha Pondthai, Mark E. Everett, Anca Avram, Alida Timar-Gabor, et al. "Groundwater erosion of coastal gullies along the Canterbury coast (New Zealand): a rapid and episodic process controlled by rainfall intensity and substrate variability." Earth Surface Dynamics 9, no. 1 (January 8, 2021): 1–18. http://dx.doi.org/10.5194/esurf-9-1-2021.
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Abstract. Gully formation has been associated to groundwater seepage in unconsolidated sand- to gravel-sized sediments. Our understanding of gully evolution by groundwater seepage mostly relies on experiments and numerical simulations, and these rarely take into consideration contrasts in lithology and permeability. In addition, process-based observations and detailed instrumental analyses are rare. As a result, we have a poor understanding of the temporal scale of gully formation by groundwater seepage and the influence of geological heterogeneity on their formation. This is particularly the case for coastal gullies, where the role of groundwater in their formation and evolution has rarely been assessed. We address these knowledge gaps along the Canterbury coast of the South Island (New Zealand) by integrating field observations, luminescence dating, multi-temporal unoccupied aerial vehicle and satellite data, time domain electromagnetic data and slope stability modelling. We show that gully formation is a key process shaping the sandy gravel cliffs of the Canterbury coastline. It is an episodic process associated to groundwater flow that occurs once every 227 d on average, when rainfall intensities exceed 40 mm d−1. The majority of the gullies in a study area southeast (SE) of Ashburton have undergone erosion, predominantly by elongation, during the last 11 years, with the most recent episode occurring 3 years ago. Gullies longer than 200 m are relict features formed by higher groundwater flow and surface erosion > 2 ka ago. Gullies can form at rates of up to 30 m d−1 via two processes, namely the formation of alcoves and tunnels by groundwater seepage, followed by retrogressive slope failure due to undermining and a decrease in shear strength driven by excess pore pressure development. The location of gullies is determined by the occurrence of hydraulically conductive zones, such as relict braided river channels and possibly tunnels, and of sand lenses exposed across sandy gravel cliffs. We also show that the gully planform shape is generally geometrically similar at consecutive stages of evolution. These outcomes will facilitate the reconstruction and prediction of a prevalent erosive process and overlooked geohazard along the Canterbury coastline.
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Cama, Mariaelena, Calogero Schillaci, Jan Kropáček, Volker Hochschild, Alberto Bosino, and Michael Märker. "A Probabilistic Assessment of Soil Erosion Susceptibility in a Head Catchment of the Jemma Basin, Ethiopian Highlands." Geosciences 10, no. 7 (June 27, 2020): 248. http://dx.doi.org/10.3390/geosciences10070248.
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Soil erosion represents one of the most important global issues with serious effects on agriculture and water quality, especially in developing countries, such as Ethiopia, where rapid population growth and climatic changes affect widely mountainous areas. The Meskay catchment is a head catchment of the Jemma Basin draining into the Blue Nile (Central Ethiopia) and is characterized by high relief energy. Thus, it is exposed to high degradation dynamics, especially in the lower parts of the catchment. In this study, we aim at the geomorphological assessment of soil erosion susceptibilities. First, a geomorphological map was generated based on remote sensing observations. In particular, we mapped three categories of landforms related to (i) sheet erosion, (ii) gully erosion, and (iii) badlands using a high-resolution digital elevation model (DEM). The map was validated by a detailed field survey. Subsequently, we used the three categories as dependent variables in a probabilistic modelling approach to derive the spatial distribution of the specific process susceptibilities. In this study we applied the maximum entropy model (MaxEnt). The independent variables were derived from a set of spatial attributes describing the lithology, terrain, and land cover based on remote sensing data and DEMs. As a result, we produced three separate susceptibility maps for sheet and gully erosion as well as badlands. The resulting susceptibility maps showed good to excellent prediction performance. Moreover, to explore the mutual overlap of the three susceptibility maps, we generated a combined map as a color composite where each color represents one component of water erosion. The latter map yields useful information for land-use managers and planning purposes.
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Meyer, A., and J. A. Martı́nez-Casasnovas. "Prediction of existing gully erosion in vineyard parcels of the NE Spain: a logistic modelling approach." Soil and Tillage Research 50, no. 3-4 (May 1999): 319–31. http://dx.doi.org/10.1016/s0167-1987(99)00020-3.
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Arabameri, Alireza, Biswajeet Pradhan, and Khalil Rezaei. "Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models." Geosciences Journal 23, no. 4 (June 27, 2019): 669–86. http://dx.doi.org/10.1007/s12303-018-0067-3.
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Arabameri, Alireza, Omid Asadi Nalivan, Subodh Chandra Pal, Rabin Chakrabortty, Asish Saha, Saro Lee, Biswajeet Pradhan, and Dieu Tien Bui. "Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility." Remote Sensing 12, no. 17 (September 1, 2020): 2833. http://dx.doi.org/10.3390/rs12172833.
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The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.
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Hughes, Andrew O., and Jacky C. Croke. "Validation of a spatially distributed erosion and sediment yield model (SedNet) with empirically derived data from a catchment adjacent to the Great Barrier Reef Lagoon." Marine and Freshwater Research 62, no. 8 (2011): 962. http://dx.doi.org/10.1071/mf11030.
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The use of spatially distributed erosion and sediment yield models has become a common method to assess the impacts of land-use changes within catchments and determine appropriate management options. Lack of model validation is, however, increasingly recognised as an issue, especially for models applied at the large-catchment or regional scale. The present study applies the spatially distributed erosion and sediment yield model SedNet to a 6000-km2 subcatchment of the Fitzroy River in central Queensland, Australia. Model outputs are compared with the results from sediment-source tracing, measured floodplain deposition rates and available hydrometric station data. Results indicated that significant improvement can be made to model predictions when catchment-specific observations (such as river bank and gully geometry and gully erosion history) are used to refine model-input parameters. It was also shown that the use of generic input parameters used by previous SedNet applications within the Great Barrier Reef catchment area resulted in overestimates of sediment yields. Previous model applications may have overestimated the significance of post-European catchment disturbance on the sediment yields of the dry-tropical catchments draining to the Great Barrier Reef. Our findings illustrated the value of obtaining empirically derived data to validate spatially distributed models applied at large scales.
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Bufalo, Maurice, and Daniel Nahon. "Erosional processes of Mediterranean badlands: a new erosivity index for predicting sediment yield from gully erosion." Geoderma 52, no. 1-2 (January 1992): 133–47. http://dx.doi.org/10.1016/0016-7061(92)90079-m.
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Onyelowe, Kennedy C., Ahmed M. Ebid, and Light Nwobia. "Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming." Applied and Environmental Soil Science 2021 (September 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/2630123.
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Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. Three evolutionary trials were executed, and three models were presented considering different permutations of the predictors. The performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.
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CHEN, NING-SHENG, GUI-SHENG HU, MING-FENG DENG, WEI ZHOU, CHENG-LIN YANG, D. HAN, and JIAN-HUI DENG. "IMPACT OF EARTHQUAKE ON DEBRIS FLOWS — A CASE STUDY ON THE WENCHUAN EARTHQUAKE." Journal of Earthquake and Tsunami 05, no. 05 (December 2011): 493–508. http://dx.doi.org/10.1142/s1793431111001212.
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This paper describes a study about the impact of earthquakes on debris flows with a focus on the Great Wenchuan Earthquake 2008 in China. The land form, precipitation, and source material are the three key factors for debris flow initiation in the Wenchuan surrounding area. Classifications and examples of four types of debris flow initiation triggering (gully triggering, slope triggering, liquefaction triggering, and gully erosion triggering) have been presented. The initiation mechanisms are attributed to hydraulic and geomechanical aspects. The actual debris flow cases linked with the Great Wenchuan Earthquake and other earthquakes in China have been used to illustrate the increased magnitudes of debris flows due to a large amount of loose materials created by the seismic actions. The critical precipitation for debris flows is reduced by the earthquake. It is predicted that the impact of the Great Wenchuan Earthquake on the local debris flows would be significant in the next 5–6 years, and much less in the following years (up to 20 years). Finally, the debris flow system will reach a relative stable stage. This prediction is based on the historical observations at other earthquake areas and the qualitative analysis on debris flow initiation mechanisms.
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mohammad abbas daoudi, mohammad abbas daoudi. "Numerical Modeling for Gully Erosion in the North of Algeria based on Data of Remote Sensing and Geographic Information Systems." journal of king abdulaziz university arts and humanities 25, no. 1 (February 23, 2017): 37–63. http://dx.doi.org/10.4197/art.25-1.2.
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The problems of soil erosion are largely widespread in the countries of the Mediterranean basin. The process of gullying is a complex phenomenon with disastrous consequences. It particularly affects northern Algeria, decreasing the potentialities of the water tanks, reducing cultivable lands availability and degrading infrastructures. Therefore, this work studies the analysis and the prediction of gullying erosion by using a probabilistic approach based on multisource data. The objective of this search is to answer to the three following questions: i) which factors support the process of gullying ? ii) how does a process of gullying develop? iii) which are the zones favourable to gullying ? Works are undertaken on the catchment area of the Isser River. We focused the applications on the upstream part of the basin. In this research, we study a North-South transect which corresponds to three under-basins slopes. The choice of these tests areas answers to four criteria defined in our method: the representativeness, the homogeneity, the availability of former data and, finally, the accessibility. After the completion of the multisource data, modelling and multivariate analysis for the prediction of gullying. The combination factor-process by the univariate analysis allows on the one hand, to highlight the variables controlling the process of gullying, and on the other hand, to analyse the variables on a hierarchical basis and to know their degree of influence. The multivariate analysis, by the logistic regression model (LRM), enabled us to select the significant variables and to locate the most favourable zones for the process of gullying. The validation of the models is evaluated using the curves of lift spin. The results suggest that the factors highlighted by the model to be most influential on gullying erosion are: the lithology, the slope, the morphopedology, the rainfall erosivity and the land cover. The synthesis of this approach is illustrated in the form of charts of gullying erosion risk maps in four classes of probability. The assessment of the study shows the fundamental interest of this approach using geographical information systems and remote sensing, in particular for the watersheds of the southern Mediterranean, with the possibility of extending this methodology to other regions.
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Abdallah, C., and R. B. kheir. "A quantitative model for predicting gully erosion risk in karstified Mediterranean environments: Lebanon case study." Journal of Soil and Water Conservation 64, no. 2 (March 1, 2009): 67A. http://dx.doi.org/10.2489/jswc.64.2.67a.
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Rahmati, Omid, Nasser Tahmasebipour, Ali Haghizadeh, Hamid Reza Pourghasemi, and Bakhtiar Feizizadeh. "Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion." Geomorphology 298 (December 2017): 118–37. http://dx.doi.org/10.1016/j.geomorph.2017.09.006.
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Tekwa, Ijasini John, John Matthew Laflen, and Abubakar Musa Kundiri. "Efficiency test of adapted EGEM model in predicting ephemeral gully erosion around Mubi, Northeast Nigeria." International Soil and Water Conservation Research 3, no. 1 (March 2015): 15–27. http://dx.doi.org/10.1016/j.iswcr.2015.04.001.
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Benavidez, Rubianca, Bethanna Jackson, Deborah Maxwell, and Kevin Norton. "A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates." Hydrology and Earth System Sciences 22, no. 11 (November 27, 2018): 6059–86. http://dx.doi.org/10.5194/hess-22-6059-2018.
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Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for soil loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).