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Journal articles on the topic 'Gene expression programming (GEP) and the artificial neural networks (ANNs)'
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1
Jumaa, Ghazi Bahroz, and Ali Ramadhan Yousif. "Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis." Advances in Civil Engineering 2018 (November 15, 2018): 1–16. http://dx.doi.org/10.1155/2018/5157824.
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The shear strength prediction of fiber-reinforced polymer- (FRP-) reinforced concrete beams is one of the most complicated issues in structural engineering applications. Developing accurate and reliable prediction models is necessary and cost saving. This paper proposes three new prediction models, utilizing artificial neural networks (ANNs) and gene expression programming (GEP), as a recently developed artificial intelligent techniques, and nonlinear regression analysis (NLR) as a conventional technique. For this purpose, a large database including 269 shear test results of FRP-reinforced concrete members was collected from the literature. The performance of the proposed models is compared with a large number of available codes and previously proposed equations. The comparative statistical analysis confirmed that the ANNs, GEP, and NLR models, in sequence, showed excellent performance, great efficiency, and high level of accuracy over all other existing models. The ANNs model, and to a lower level the GEP model, showed the superiority in accuracy and efficiency, while the NLR model showed that it is simple, rational, and yet accurate. Additionally, the parametric study indicated that the ANNs model defines accurately the interaction of all parameters on shear capacity prediction and have a great ability to predict the actual response of each parameter in spite of its complexity and fluctuation nature.
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Khan, Mujahid, H. Md Azamathulla, and M. Tufail. "Gene-expression programming to predict pier scour depth using laboratory data." Journal of Hydroinformatics 14, no. 3 (November 8, 2011): 628–45. http://dx.doi.org/10.2166/hydro.2011.008.
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Prediction of bridge pier scour depth is essential for safe and economical bridge design. Keeping in mind the complex nature of bridge scour phenomenon, there is a need to properly address the methods and techniques used to predict bridge pier scour. Up to the present, extensive research has been carried out for pier scour depth prediction. Different modeling techniques have been applied to achieve better prediction. This paper presents a new soft computing technique called gene-expression programming (GEP) for pier scour depth prediction using laboratory data. A functional relationship has been established using GEP and its performance is compared with other artificial intelligence (AI)-based techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Laboratory data containing 529 datasets was divided into calibration and validation sets. The performance of GEP was found to be highly satisfactory and encouraging when compared to regression equations but was slightly inferior to ANN. This slightly inferior performance of GEP compared to ANN is offset by its capability to provide compact and explicit mathematical expression for bridge scour. This advantage of GEP over ANN is the main motivation for this work. The resulting GEP models will add to the existing literature of AI-based inductive models for bridge scour modeling.
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Farooq, Furqan, Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Rehan Sadiq, Muhammad Faisal Faisal Javed, Fahid Aslam, and Rayed Alyousef. "A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)." Applied Sciences 10, no. 20 (October 20, 2020): 7330. http://dx.doi.org/10.3390/app10207330.
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Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R2 = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.
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Emadi, Alireza, Sarvin Zamanzad-Ghavidel, Reza Sobhani, and Ali Rashid-Niaghi. "Multivariate modeling of groundwater quality using hybrid evolutionary soft-computing methods in various climatic condition areas of Iran." Journal of Water Supply: Research and Technology-Aqua 70, no. 3 (March 10, 2021): 328–41. http://dx.doi.org/10.2166/aqua.2021.150.
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Abstract In the current study, several soft-computing methods including artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and hybrid wavelet theory-GEP (WGEP) are used for modeling the groundwater's electrical conductivity (EC) variable. Hence, the groundwater samples from three sources (deep well, semi-deep well, and aqueducts), located in six basins of Iran (Urmia Lake (UL), Sefid-rud (SR), Karkheh (K), Kavir-Markazi (KM), Gavkhouni (G), and Hamun-e Jaz Murian (HJM)) with various climate conditions, were collected during 2004–2018. The results of the WGEP model with data de-noising showed the best performance in estimating the EC variable, considering all types of groundwater resources with various climatic conditions. The Root Mean Squared Error (RMSE) values of the WGEP model were varied from 162.068 to 348.911, 73.802 to 171.376, 29.465 to 351.489, 118.149 to 311.798, 217.667 to 430.730, and 76.253 to 162.992 μScm−1 in the areas of UL, SR, K, KM, G, and HJM basins. The WGEP model's performance (R-values) for deep wells, semi-deep wells, and aqueducts of the areas of the KM basin associated with the arid steppe cold (Bsk) dominant climate classification was the best. Also, the WGEP's extracted mathematical equations could be used for EC estimating in other basins.
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SBAIH, Razan, Rana IMAM, Mohammad ALHIARY, and Bara’ AL-MISTAREHI. "DEVELOPING PREDICTION MODELS FOR SLOPE VARIANCE FROM THE INTERNATIONAL ROUGHNESS INDEX." Transport Problems 17, no. 2 (June 30, 2022): 93–106. http://dx.doi.org/10.20858/tp.2022.17.2.08.
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Road roughness is considered a primary indicator of pavement condition and serviceability, and the performance of paved roads is linked to road roughness. The focus of this study is to develop a relationship between two important roughness indicators, namely the international roughness index (IRI) and slope variance (SV), based on actual road roughness data to achieve a suitable correlation between these two indices using artificial neural networks (ANNa) and gene expression programming (GEP) techniques. Different study areas were selected to develop the prediction model. The first study area is the Desert Highway in Jordan, while the three remaining study areas are located in the US. A total of 533 data sets were used in this study to develop a model to predict the IRI from the SV. The GeneXproTools 5 software package was used to build the GEP model, while MATLAB 2019 was employed to develop the ANN model. The results showed that the GEP and ANN models outperformed all other previous models. The GEP-Based model showed a better performance and more precise results than the ANN model according to the coefficient of determination (R2).
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Samadianfard, Saeed, Honeyeh Kazemi, Ozgur Kisi, and Wen-Cheng Liu. "Water temperature prediction in a subtropical subalpine lake using soft computing techniques." Earth Sciences Research Journal 20, no. 2 (July 1, 2016): 1. http://dx.doi.org/10.15446/esrj.v20n2.43199.
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Lake water temperature is one of the key parameters in determining the ecological conditions within a lake, as it influences both chemical and biological processes. Therefore, accurate prediction of water temperature is crucially important for lake management. In this paper, the performance of soft computing techniques including gene expression programming (GEP), which is a variant of genetic programming (GP), adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) to predict hourly water temperature at a buoy station in the Yuan-Yang Lake (YYL) in north-central Taiwan at various measured depths was evaluated. To evaluate the performance of the soft computing techniques, three different statistical indicators were used, including the root mean squared error (RMSE), the mean absolute error (MAE), and the coefficient of correlation (R). Results showed that the GEP had the best performances among other studied methods in the prediction of hourly water temperature at 0, 2 and 3 meter depths below water surface, but there was a different trend in the 1 meter depth below water surface. In this depth, the ANN had better accuracy than the GEP and ANFIS. Despite the error (RMSE value) is smaller in ANN than GEP, there is an upper bound in scatter plot of ANN that imposes a constant value, which is not suitable for predictive purposes. As a conclusion, results from the current study demonstrated that GEP provided moderately reasonable trends for the prediction of hourly water temperature in different depths. ResumenLa temperatura del agua es uno de los parámetros básicos para determinar las condiciones ecológicas de un lago, ya que está influenciada por procesos químicos y biológicos. Además, la exactitud en la predicción de la temperatura del agua es esencial para el manejo del lago. En este artículo se evalúa el desempeño de técnicas de soft computing como la Programación de Expresiones de Genes (PEG), que es una variante de la Programación Genética (PG), el Sistema Neuro-fuzzy de Inferencia Adaptativa (Anfis, en inglés) y las Redes Neuronales Artificiales (RNA) para predecir la temperatura del agua en diferentes niveles de una estación flotante del lago Yuan-Yang (YYL), en el centro-norte de Taiwán. Se utilizaron tres indicadores estadísticos, el Error Cuadrático Medio (ECM), el Error Absoluto Medio (MAE, en inglés) y el Coeficiente de Correlación (R) para evaluar el desempeño de las técnicas de computación. Los resultados muestran que la PEG es más exacta en la predicción de la temperatura del agua entre 0,2 y 3 metros de profundidad. Sin embargo, se evidencia una tendencia diferente a partir del metro de profundidad. A esta distancia de la superficie, las RNA son más exactas que la PEG y el Anfis. Los resultados de este estudio probaron claramente la usabilidad del PEG y las RNA en la predicción de la temperatura del agua a diferentes profundidades.
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Wang, Xiao Yong. "Evaluation Compressive Strength of Cement-Limestone-Slag Ternary Blended Concrete Using Artificial Neural Networks (ANN) and Gene Expression Programming (GEP)." Key Engineering Materials 837 (April 2020): 119–24. http://dx.doi.org/10.4028/www.scientific.net/kem.837.119.
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Limestone and slag blended concrete is an innovative concrete which belongs to the family of limestone calcined clay cement (LC3) concrete. Strength is an important property of structural concrete. This study shows artificial neural networks (ANN) and gene expression programming (GEP) models for predicting strength development of limestone and slag blended concrete. ANN model consists of an input layer, a hidden layer, and output layer. GEP model consists of the sum of three expression trees. The input parameters of ANN and GEP models are mixtures and ages. The output parameter is a strength. The correlation coefficients of ANN and GEP model are 0.99 and 0.98, respectively. Both ANN and GEP model can produce prediction results of the strength of ternary blended concrete reliably.
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Shiri, Jalal, Sungwon Kim, and Ozgur Kisi. "Estimation of daily dew point temperature using genetic programming and neural networks approaches." Hydrology Research 45, no. 2 (August 17, 2013): 165–81. http://dx.doi.org/10.2166/nh.2013.229.
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The present study investigates the ability of two different artificial neural network (ANN) models and gene expression programming (GEP) technique for estimating daily dew point temperature by using recorded weather data. The weather data used consist of 8 years of daily records of air temperature, wind speed, relative humidity, atmospheric pressure, incoming solar radiation and dew point temperature from two weather stations (Seoul and Incheon, in the Republic of Korea). Two different data management scenarios are applied in this paper. In the first scenario, weather data obtained from each station are used to estimate Tdew at the same station (at-station approach). In the second scenario, the ANN and GEP models are used for estimating dew point temperature of each station by using the data of the other station (cross-station application), through the optimal input combinations of the first scenario. Comparison of the results reveals that the GEP model surpasses ANN in estimating daily dew point temperature values.
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Ashteyat, Ahmed, Yasmeen T. Obaidat, Yasmin Z. Murad, and Rami Haddad. "COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 26, no. 2 (February 10, 2020): 189–99. http://dx.doi.org/10.3846/jcem.2020.11931.
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The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.
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Ilie, Iulia, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, et al. "Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming." Geoscientific Model Development 10, no. 9 (September 25, 2017): 3519–45. http://dx.doi.org/10.5194/gmd-10-3519-2017.
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Abstract. Accurate model representation of land–atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented by a steadily evolving body of mechanistic theory, provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates readable models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions, with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (random forests, support vector machines, artificial neural networks, and kernel ridge regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-eastern England. We find that the GEP-retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components, the identification of a general terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data-rich era, complementing more traditional modelling approaches.
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DOBRUCALI, Esra, and İsmail Hakkı DEMİR. "COMPARISON OF GENE EXPRESSION PROGRAMMING AND ARTIFICIAL NEURAL NETWORK TECHNIQUES FOR ESTIMATING BUILDING COST." INTERNATIONAL REFEREED JOURNAL OF ENGINEERING AND SCIENCES, no. 16 (2022): 0. http://dx.doi.org/10.17366/uhmfd.2022.16.2.
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Aim: Construction project, cost calculation based on bill of materials is generally accepted as a classical method in terms of giving definite result. However, because the calculation of each work item is required, there is a need for a long calculation time. In this study, is worked on the availability of practical, fast and realistic results using artificial intelligence techniques with a few determined variables without performing detailed quantification works in the public institutions budget planning or in the tenderer’s project cost estimate calculations; finally, the obtained results are compared. Method: For this purpose, it is aimed to estimate the project cost by using Gene Expression Programming (GEP) technique and Artificial Neural Network (ANN) techniques by using variables such as the number of floors, duration, building type and total construction area of 75 education and health building projects carried out between 2011 and 2016 (60 of which are training,15 are test data). Results: According to the test data obtained at the end of the study, the project cost determination coefficient (R2) was 0.970 with the Gene Expression Programming technique and 0.967 with the artificial neural network technique. Conclusion: The study shows that Gene Expression Programming and Artificial Neural Networks techniques can be used equally in building cost calculations, and both methods give acceptable values close to reality.
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Rizvi, Zarghaam Haider, Syed Jawad Akhtar, Syed Mohammad Baqir Husain, Mohiuddeen Khan, Hasan Haider, Sakina Naqvi, Vineet Tirth, and Frank Wuttke. "Neural Network Approaches for Computation of Soil Thermal Conductivity." Mathematics 10, no. 21 (October 25, 2022): 3957. http://dx.doi.org/10.3390/math10213957.
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The effective thermal conductivity (ETC) of soil is an essential parameter for the design and unhindered operation of underground energy transportation and storage systems. Various experimental, empirical, semi-empirical, mathematical, and numerical methods have been tried in the past, but lack either accuracy or are computationally cumbersome. The recent developments in computer science provided a new computational approach, the neural networks, which are easy to implement, faster, versatile, and reasonably accurate. In this study, we present three classes of neural networks based on different network constructions, learning and computational strategies to predict the ETC of the soil. A total of 384 data points are collected from literature, and the three networks, Artificial neural network (ANN), group method of data handling (GMDH) and gene expression programming (GEP), are constructed and trained. The best accuracy of each network is measured with the coefficient of determination (R2) and found to be 91.6, 83.2 and 80.5 for ANN, GMDH and GEP, respectively. Furthermore, two sands with 80% and 99% quartz content are measured, and the best performing network from each class of ANN, GMDH and GEP is independently validated. The GEP model provided the best estimate for 99% quartz sand and GMDH with 80%.
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Wang, Ruliang, and Benbo Zha. "A Research on the Optimal Design of BP Neural Network Based on Improved GEP." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (February 19, 2019): 1959007. http://dx.doi.org/10.1142/s0218001419590079.
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Due to the functionality of dynamic mapping for nonlinear complex data, BP neural network (BP-NN) as a typical neural network has increasingly been applied to a variety of applications. Although it has been successfully applied, its prominent shortcoming, such as the local optimum problem and the setting problem for the initial parameter of neural network, have not been completely eliminated. In this paper, an optimization algorithm for the architecture, weights and thresholds of neural networks using an improved gene expression programming (IGEP) was presented. The algorithm effectively combines the global search ability of GEP and the local search ability of BP-NN. To obtain a better efficiency, the basic GEP was improved by the dynamic adjustment of the fitness function, genetic operators and the number of evolutionary generations. The experimental results show that the IGEP-BP algorithm is an effective method for evolving neural network.
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Ilyas, Israr, Adeel Zafar, Muhammad Talal Afzal, Muhammad Faisal Javed, Raid Alrowais, Fadi Althoey, Abdeliazim Mustafa Mohamed, Abdullah Mohamed, and Nikolai Ivanovich Vatin. "Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of FRP Confined Concrete Using Multiphysics Genetic Expression Programming." Polymers 14, no. 9 (April 27, 2022): 1789. http://dx.doi.org/10.3390/polym14091789.
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The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.
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Liu, Li-Wei, Chun-Tang Lu, Yu-Min Wang, Kuan-Hui Lin, Xingmao Ma, and Wen-Shin Lin. "Rice (Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms." Agriculture 12, no. 1 (January 3, 2022): 59. http://dx.doi.org/10.3390/agriculture12010059.
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Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.
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Kontoni, Denise-Penelope N., Kennedy C. Onyelowe, Ahmed M. Ebid, Hashem Jahangir, Danial Rezazadeh Eidgahee, Atefeh Soleymani, and Chidozie Ikpa. "Gene Expression Programming (GEP) Modelling of Sustainable Building Materials including Mineral Admixtures for Novel Solutions." Mining 2, no. 4 (September 21, 2022): 629–53. http://dx.doi.org/10.3390/mining2040034.
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In this study, the employment of the gene expression programming (GEP) technique in forecasting models on sustainable construction materials including mineral admixtures and civil engineering quantities (e.g., compressive strength), was investigated. Compared to the artificial neural networks (ANN) based formulations, which are often too complicated to be used, GEP-based derived models provide estimation equations that are reasonably simple and may be used for practical design purposes and even for hand calculations. Many popular models, such as best-fitted curves based on regression analyses, multi-linear regression (MLR), multinomial logistic regression (MNLR), and multinomial variate regression (MNVR), can also be used for construction materials properties modeling. However, due to the nonlinearity and complexity of the target properties, the models established using linear regression analyses may not reveal the precise behavior. Additionally, regression models lack generality, and this comes from the fact that some functions are defined for regression in classical regression techniques; while in the GEP approach, there is no predefined function to be considered, and it reproduces or omits various combinations of parameters to provide the formulation that fits the experimental outcomes. If the input parameters can be evaluated through simple laboratory or rapid measurements, and also a comprehensive experimental database is made available, the models can be constructed with optimal flexibility. Flexibility in choosing the complexity and fitness functions, such as RMSE, MAE, and MSE, might lead to better performance of the approach and well-capturing the governing pattern behind the material’s characteristics. There may be minor inaccuracies with this technique; however, the explicit mathematical expressions, which can be easily implemented in the design and analysis process, may cover the minor inaccuracies compared to ANN, support vector machine (SVM), and other intelligent approaches. Based on the presented study, sometimes it would be better to provide more than one GEP model and consider different combinations of input contributing variables to afford the possible initial feed for a more settled and comprehensive model. Mostly, GEP’s strengths as a superior machine learning technique in modeling the behavior of construction materials including mineral admixtures, leading to innovative solutions in civil engineering, have been presented.
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Daryaee, Mehdi, Farshad Ahmadi, Peyman Peykani, and Mohammadreza Zayeri. "Prediction of longitudinal and transverse profiles of pressure flushing cones using artificial intelligence and data pre-processing." Water Supply 22, no. 2 (September 29, 2021): 1533–45. http://dx.doi.org/10.2166/ws.2021.333.
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Abstract One of the most critical issues in dam reservoir management is the determination of sediment level after flushing operation. Artificial intelligence (AI) methods have recently been considered in this context. The present study adopts four AI approaches, including the Feed-Forward Neural Network (FFNN), Cascade Feed-Forward Neural Network (CFFNN), Gene Expression Programming (GEP), and Bayesian Networks (BNs). Experimental data were exploited to train and test the models. The results revealed that the models were able to estimate the post-flushing sediment level accurately. FFNN outperformed the other models. Furthermore, the importance of model inputs was determined using the τ-Kendall (τ–k), Random Forest (RF), and Shannon Entropy (SE) pre-processing methods. The initial level of sediment was found to be the most important input, while the orifice output flow rate was observed to have the lowest importance in modeling. Finally, inputs of higher weights were introduced to the FFNN model (as the best predictive model), and the analysis of the results indicated that the exclusion of less important input variables would have no significant impact on model performance.
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Emamgholizadeh, Samad, and Razieh Karimi Demneh. "A comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on the Telar and Kasilian rivers in Iran." Water Supply 19, no. 1 (March 17, 2018): 165–78. http://dx.doi.org/10.2166/ws.2018.062.
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Abstract The estimation of the suspended sediment load in rivers is one of the main issues in hydraulic engineering. Different traditional methods such as the sediment rating curve (SRC) can be used to estimate the suspended sediment load of rivers. The main problem with this method is its low accuracy and uncertainty. In this study, the ability of three intelligence models namely: gene expression programming (GEP), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) were compared with the SRC method. The daily flow discharge and sediment discharge at two hydrometric stations of the Kasilian and Telar rivers in the period of 1964–2014 were used to develop intelligence models. The performance of these methods indicated that all intelligence models give reliable results in the estimation of the suspended sediment load and their performance was better than the SRC method. Moreover, results showed that the GEP model with a high coefficient of determination (R2) and a low mean absolute error (MAE) was better than both the ANN and ANFIS models for the estimation of daily suspended sediment load of the two sub-basins of the Kasilian and Telar rivers.
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Amin, Muhammad Nasir, Izaz Ahmad, Asim Abbas, Kaffayatullah Khan, Muhammad Ghulam Qadir, Mudassir Iqbal, Abdullah Mohammad Abu-Arab, and Anas Abdulalim Alabdullah. "Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches." Materials 15, no. 17 (August 26, 2022): 5908. http://dx.doi.org/10.3390/ma15175908.
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This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks—normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.
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Kasten, Christian, Junsu Shin, Richard D. Sandberg, Michael Pfitzner, Nilanjan Chakraborty, and Markus Klein. "Modelling Subgrid-scale Scalar Dissipation Rate in Turbulent Premixed Flames using Gene Expression Programming and Deep Artificial Neural Networks." Physics of Fluids, July 12, 2022. http://dx.doi.org/10.1063/5.0095886.
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In the present study, Gene Expression Programming (GEP) will be used for training a model for subgrid scale (SGS) scalar dissipation rate (SDR) for a large range of filter widths, using a database of statistically planar turbulent premixed flames, featuring different turbulence intensities and heat release parameters. GEP is based on the idea to iteratively improve a population of model candidates using the survival-of-the-fittest concept. The resulting model is a mathematical expression that can be easily implemented, shared with the community and analyzed for physical consistency, as illustrated in this work. Efficient evaluation of the cost function and a smart choice of basis functions have been found to be essential for a successful optimization process. The GEP based model has been found to outperform an existing algebraic model from the literature. However, the optimization process was found to be quite intricate and the SGS SDR closure turned out to be difficult. Some of these problems have been explained using the model-agnostic interpretation method which requires the existence of a trained artificial neural network (ANN). ANNs are known for their ability to represent complex functional relationships and serve as an additional benchmark solution for the GEP based model.
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Narang, Aishwarya, Ravi Kumar, and Amit Dhiman. "Machine learning applications to predict the axial compression capacity of concrete filled steel tubular columns: a systematic review." Multidiscipline Modeling in Materials and Structures, December 30, 2022. http://dx.doi.org/10.1108/mmms-09-2022-0195.
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PurposeThis study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).Design/methodology/approachConcrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.FindingsThe implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.Originality/valueThis study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.
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Emadi, Alireza, Reza Sobhani, Hossein Ahmadi, Arezoo Boroomandnia, Sarvin Zamanzad-Ghavidel, and Hazi Mohammad Azamathulla. "Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method." Water Supply, July 15, 2021. http://dx.doi.org/10.2166/ws.2021.224.
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Abstract Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned factors, have been used to estimate River Water Withdrawal (RWW) for agricultural uses. Lavasanat and Qazvin are selected as study areas, located in the Namak Lake basin in Iran, with Bsk and Csa climate categories, respectively. Estimation of RWW is performed using single and Wavelet–hybrid (W-hybrid) data-driven methods, including Artificial Neural Networks (ANNs), Wavelet–ANN (WANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet–ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet–GEP (WGEP). Due to the evaluation criteria, the performance of the WGEP model is the best among the others for estimating RWW variables in both study areas. Considering the W-hybrid models with data de-noising for estimating RWW in the Lavasanat and Qazvin study areas, the obtained values of RMSE for WGEP11 to WGEP15 and WGEP21 to WGEP25 equal 67.268, 54.659, 80.871, 50.796, 15.676 and 105.532, 96.615, 105.018, 160.961, 44.332, respectively. The results indicate that WGEP and ANN are the best and poorest models in both study areas without regarding climate condition effects. Also, a combined factor which includes River Width (RW), minimum flow rate (QMin), average flow rate (QMean), Electrical Conductivity (EC), and Cultivated Area (CA) variables is introduced as the best factor to estimate RWW variables compared with the other factors in both the Bsk and Csa climate categories. On the other hand, qualitative hydrological and land use factors were the weakest ones to estimate RWW variables in the Bsk and Csa climate categories, respectively. Therefore, the current study explores how the mathematical relations for estimating RWW have a significant effect on water resources management and planning by policymakers in the future.
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Kaushik, Vijay, and Munendra Kumar. "Assessment of water surface profile in nonprismatic compound channels using machine learning techniques." Water Supply, December 17, 2022. http://dx.doi.org/10.2166/ws.2022.430.
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Abstract Accurate prediction of water surface profile in an open channel is the key to solving numerous critical engineering problems. The goal of the current research is to predict the water surface profile of a compound channel with converging floodplains using machine learning approaches, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), in terms of both geometric and flow variables, as past studies were more focused on geometric variables. A novel equation was also proposed using gene expression programming to predict the water surface profile. In order to evaluate the performance and efficacy of these models, statistical indices are used to validate the produced models for the experimental analysis. The findings demonstrate that the suggested ANN model accurately predicted the water surface profile, with coefficient of determination (R2) of 0.999, root mean square error (RMSE) of 0.003, and mean absolute percentage error (MAPE) of 0.107%, respectively, when compared to GEP, SVM, and previously developed methods. The study confirms the application of machine learning approaches in the field of river hydraulics, and forecasting water surface profile of nonprismatic compound channels using a proposed novel equation by gene expression programming made this study unique.
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Hanandeh, Shadi, Ahmad Hanandeh, Mohammad Alhiary, and Mohammad Al Twaiqat. "Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling." Frontiers in Built Environment 8 (October 21, 2022). http://dx.doi.org/10.3389/fbuil.2022.895210.
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The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.
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Afradi, Alireza, and Arash Ebrahimabadi. "Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate." SN Applied Sciences 2, no. 12 (November 17, 2020). http://dx.doi.org/10.1007/s42452-020-03767-y.
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Bozorg-Haddad, Omid, Sahar Baghban, and Hugo A. Loáiciga. "Assessment of global hydro-social indicators in water resources management." Scientific Reports 11, no. 1 (August 31, 2021). http://dx.doi.org/10.1038/s41598-021-96776-9.
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AbstractWater is a vital element that plays a central role in human life. This study assesses the status of indicators based on water resources availability relying on hydro-social analysis. The assessment involves countries exhibiting decreasing trends in per capita renewable water during 2005–2017. Africa, America, Asia, Europe, and Oceania encompass respectively 48, 35, 43, 20, and 5 countries with distinct climatic conditions. Four hydro-social indicators associated with rural society, urban society, technology and communication, and knowledge were estimated with soft-computing methods [i.e., artificial neural networks, adaptive neuro-fuzzy inference system, and gene expression programming (GEP)] for the world’s continents. The GEP model’s performance was the best among the computing methods in estimating hydro-social indicators for all the world’s continents based on statistical criteria [correlation coefficient (R), root mean square error (RMSE), and mean absolute error]. The values of RMSE for GEP models for the ratio of rural to urban population (PRUP), population density, number of internet users and education index parameters equaled (0.084, 0.029, 0.178, 0.135), (0.197, 0.056, 0.152, 0.163), (0.151, 0.036, 0.123, 0.210), (0.182, 0.039, 0.148, 0.204) and (0.141, 0.030, 0.226, 0.082) for Africa, America, Asia, Europe and Oceania, respectively. Scalable equations for hydro-social indicators are developed with applicability at variable spatial and temporal scales worldwide. This paper’s results show the patterns of association between social parameters and water resources vary across continents. This study’s findings contribute to improving water-resources planning and management considering hydro-social indicators.
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Heddam, Salim, Hadi Sanikhani, and Ozgur Kisi. "Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study." Applied Water Science 9, no. 7 (September 30, 2019). http://dx.doi.org/10.1007/s13201-019-1044-3.
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Abstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.