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Journal articles on the topic 'Random forest regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Rigatti, Steven J. "Random Forest." Journal of Insurance Medicine 47, no. 1 (January 1, 2017): 31–39. http://dx.doi.org/10.17849/insm-47-01-31-39.1.

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For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become increasingly common. Particularly in this era of “big data” and machine learning, survival analysis has become methodologically broader. This paper aims to explore one technique known as Random Forest. The Random Forest technique is a regression tree technique which uses bootstrap aggregation and randomization of predictors to achieve a high degree of predictive accuracy. The various input parameters of the random forest are explored. Colon cancer data (n = 66,807) from the SEER database is then used to construct both a Cox model and a random forest model to determine how well the models perform on the same data. Both models perform well, achieving a concordance error rate of approximately 18%.

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Kaymak, Sertan, and Ioannis Patras. "Multimodal random forest based tensor regression." IET Computer Vision 8, no. 6 (December 2014): 650–57. http://dx.doi.org/10.1049/iet-cvi.2013.0320.

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Costa, Iago Sousa Lima, Isabelle Cavalcanti Corrêa de Oliveira Serafim, Felipe Mattos Tavares, and Hugo José de Oliveira Polo. "Uranium anomalies detection through Random Forest regression." Exploration Geophysics 51, no. 5 (February 23, 2020): 555–69. http://dx.doi.org/10.1080/08123985.2020.1725387.

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4

Tsagkrasoulis, Dimosthenis, and Giovanni Montana. "Random forest regression for manifold-valued responses." Pattern Recognition Letters 101 (January 2018): 6–13. http://dx.doi.org/10.1016/j.patrec.2017.11.008.

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Pal, Mahesh, N. K. Singh, and N. K. Tiwari. "Pier scour modelling using random forest regression." ISH Journal of Hydraulic Engineering 19, no. 2 (June 2013): 69–75. http://dx.doi.org/10.1080/09715010.2013.772763.

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Mendez, Guillermo, and Sharon Lohr. "Estimating residual variance in random forest regression." Computational Statistics & Data Analysis 55, no. 11 (November 2011): 2937–50. http://dx.doi.org/10.1016/j.csda.2011.04.022.

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7

Grömping, Ulrike. "Variable Importance Assessment in Regression: Linear Regression versus Random Forest." American Statistician 63, no. 4 (November 2009): 308–19. http://dx.doi.org/10.1198/tast.2009.08199.

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A, Dr Akila, and Ms Padma R. "Breast Cancer Tumor Categorization using Logistic Regression, Decision Tree and Random Forest Classification Techniques." International Journal of Research in Arts and Science 5, Special Issue (August 30, 2019): 282–89. http://dx.doi.org/10.9756/bp2019.1002/27.

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Milanović, Slobodan, Nenad Marković, Dragan Pamučar, Ljubomir Gigović, Pavle Kostić, and Sladjan D. Milanović. "Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method." Forests 12, no. 1 (December 22, 2020): 5. http://dx.doi.org/10.3390/f12010005.

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Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.

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Sekulić, Aleksandar, Milan Kilibarda, Gerard B. M. Heuvelink, Mladen Nikolić, and Branislav Bajat. "Random Forest Spatial Interpolation." Remote Sensing 12, no. 10 (May 25, 2020): 1687. http://dx.doi.org/10.3390/rs12101687.

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For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.

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Iannace, Gino, Giuseppe Ciaburro, and Amelia Trematerra. "Wind Turbine Noise Prediction Using Random Forest Regression." Machines 7, no. 4 (November 6, 2019): 69. http://dx.doi.org/10.3390/machines7040069.

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Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coefficient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.

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Emir, Senol, Hasan Dincer, Umit Hacioglu, and Serhat Yuksel. "Random Regression Forest Model using Technical Analysis Variables." International Journal of Finance & Banking Studies (2147-4486) 5, no. 3 (July 21, 2016): 85–102. http://dx.doi.org/10.20525/ijfbs.v5i3.461.

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The purpose of this study is to explore the importance and ranking of technical analysis variables in Turkish banking sector. Random Forest method is used for determining importance scores of inputs for eight banks in Borsa Istanbul. Then two predictive models utilizing Random Forest (RF) and Artificial Neural Networks (ANN) are built for predicting BIST-100 index and bank closing prices. Results of the models are compared by three metrics namely Mean Absolute Error (MAE), Mean Square Error (MSE), Median Absolute Error (MedAE). Findings show that moving average (MAV-100) is the most important variable for both BIST -100 index and bank closing prices. Therefore, investors should follow this technical indicator with respect to Turkish banks. In addition ANN shows better performance for all metrics.

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Jog, Amod, Aaron Carass, Snehashis Roy, Dzung L. Pham, and Jerry L. Prince. "Random forest regression for magnetic resonance image synthesis." Medical Image Analysis 35 (January 2017): 475–88. http://dx.doi.org/10.1016/j.media.2016.08.009.

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Coulston, John W., Christine E. Blinn, Valerie A. Thomas, and Randolph H. Wynne. "Approximating Prediction Uncertainty for Random Forest Regression Models." Photogrammetric Engineering & Remote Sensing 82, no. 3 (March 1, 2016): 189–97. http://dx.doi.org/10.14358/pers.82.3.189.

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Gonzalo-Martin, Consuelo, Mario Lillo-Saavedra, Angel Garcia-Pedrero, Octavio Lagos, and Ernestina Menasalvas. "Daily Evapotranspiration Mapping Using Regression Random Forest Models." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 12 (December 2017): 5359–68. http://dx.doi.org/10.1109/jstars.2017.2733958.

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Han, Sunwoo, and Hyunjoong Kim. "Optimal Feature Set Size in Random Forest Regression." Applied Sciences 11, no. 8 (April 12, 2021): 3428. http://dx.doi.org/10.3390/app11083428.

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One of the most important hyper-parameters in the Random Forest (RF) algorithm is the feature set size used to search for the best partitioning rule at each node of trees. Most existing research on feature set size has been done primarily with a focus on classification problems. We studied the effect of feature set size in the context of regression. Through experimental studies using many datasets, we first investigated whether the RF regression predictions are affected by the feature set size. Then, we found a rule associated with the optimal size based on the characteristics of each data. Lastly, we developed a search algorithm for estimating the best feature set size in RF regression. We showed that the proposed search algorithm can provide improvements over other choices, such as using the default size specified in the randomForest R package and using the common grid search method.

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Munsaket, Patcharawee, Supachai Awiphan, Poemwai Chainakun, and Eamonn Kerins. "Retrieving exoplanet atmospheric parameters using random forest regression." Journal of Physics: Conference Series 2145, no. 1 (December 1, 2021): 012010. http://dx.doi.org/10.1088/1742-6596/2145/1/012010.

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Abstract Understanding of exoplanet atmospheres can be extracted from the transmission spectra using an important tool based on a retrieval technique. However, the traditional retrieval method (e.g. MCMC and nested sampling) consumes a lot of computational time. Therefore, this work aims to apply the random forest regression, one of the supervised machine learning technique, to retrieve exoplanet atmospheric parameters from the transmission spectra observed in the optical wavelength. We discovered that the random forest regressor had the best accuracy in predicting planetary radius ( R F i t 2 = 0.999) as well as acceptable accuracy in predicting planetary mass, temperature, and metallicity of planetary atmosphere. Our results suggested that the random forest regression consumes significantly less computing time while gives the predicted results equivalent to those of the nested sampling PLATON retrieval.

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18

Sang, Gaoli, Hu Chen, and Qijun Zhao. "Head Pose Estimation with Improved Random Regression Forests." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/703514.

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Perception of head pose is useful for many face-related tasks such as face recognition, gaze estimation, and emotion analysis. In this paper, we propose a novel random forest based method for estimating head pose angles from single face images. In order to improve the effectiveness of the constructed head pose predictor, we introduce feature weighting and tree screening into the random forest training process. In this way, the features with more discriminative power are more likely to be chosen for constructing trees, and each of the trees in the obtained random forest usually has high pose estimation accuracy, while the diversity or generalization ability of the forest is not deteriorated. The proposed method has been evaluated on four public databases as well as a surveillance dataset collected by ourselves. The results show that the proposed method can achieve state-of-the-art pose estimation accuracy. Moreover, we investigate the impact of pose angle sampling intervals and heterogeneous face images on the effectiveness of trained head pose predictors.

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19

Rustam, Zuherman, Fildzah Zhafarina, Glori Stephani Saragih, and Sri Hartini. "Pancreatic cancer classification using logistic regression and random forest." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 476. http://dx.doi.org/10.11591/ijai.v10.i2.pp476-481.

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<span id="docs-internal-guid-2f1ba81b-7fff-8c46-5600-cbb159235091"><span>In the medical field, technology machinery is needed to solve several classification problems. Therefore, this research is useful to solve the problem of the medical field by using machine learning. This study discusses the classification of pancreatic cancer by using regression logistics and random forest. By comparing the accuracy, precision, recall (sensitivity), and F1-score of both methods, then we will know which method is better in classifying the pancreatic cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that random forest has better accuracy than logistic regressions. It can be seen with maximum accuracy of logistic regressions 96.48 with 30% data training and random forest 99.38% with 20% of data training.</span></span>

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Ghasemi, Jahan B., and Hossein Tavakoli. "Application of random forest regression to spectral multivariate calibration." Analytical Methods 5, no. 7 (2013): 1863. http://dx.doi.org/10.1039/c3ay26338j.

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21

Mercadier, Mathieu, and Jean-Pierre Lardy. "Credit spread approximation and improvement using random forest regression." European Journal of Operational Research 277, no. 1 (August 2019): 351–65. http://dx.doi.org/10.1016/j.ejor.2019.02.005.

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Fouedjio, Francky. "Exact Conditioning of Regression Random Forest for Spatial Prediction." Artificial Intelligence in Geosciences 1 (December 2020): 11–23. http://dx.doi.org/10.1016/j.aiig.2021.01.001.

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23

Sage, Andrew J., Ulrike Genschel, and Dan Nettleton. "A residual-based approach for robust random forest regression." Statistics and Its Interface 14, no. 4 (2021): 389–402. http://dx.doi.org/10.4310/20-sii660.

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24

Goundar, Sam, and Akashdeep Bhardwaj. "Property Valuation Using Linear Regression and Random Forest Algorithm." International Journal of System Dynamics Applications 10, no. 4 (October 2021): 1–16. http://dx.doi.org/10.4018/ijsda.20211001.oa13.

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The economic boom over the recent past and the quest to further develop, has made several nation states the business hubs in their regions. Along with the investments, there has been growth in the number of property sales. Social media has become convenient platform of choice for advertising property sales after the introduction of Web 2.0. This article utilizes social media platforms like Facebook to scrape data from user groups advertising properties and then using data mining techniques and approaches to determine true valuation of properties. This methodology is based on set attributes, in the urban areas by looking at the property sales of the recent past within the same area. This enables investors interested in these properties and provides a fair idea of price of properties based on the key attributes associated with the respective property.

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Fano, Nisrina Fadhilah. "Pembuatan Model Pemeringkatan Ulasan Menggunakan Metode Random Forest Regression." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 8, no. 2 (June 17, 2021): 664–75. http://dx.doi.org/10.35957/jatisi.v8i2.777.

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Di tengah perkembangan teknologi yang cepat, internet telah mengubah gaya hidup masyarakat, seperti dalam hal berbelanja barang. Dalam melakukan pembelanjaan melalui internet, salah satu hal yang perlu diperhatikan adalah ulasan pelanggan. Permasalahan muncul ketika jumlah ulasan pelanggan yang ada sangat besar, sehingga jumlah informasi yang tersedia terlalu banyak. Untuk menyelesaikan permasalahan ini, beberapa platform belanja dalam jaringan (daring) mengurutkan ulasan pelanggan dari yang paling membantu hingga kurang membantu. Namun sistem ini memiliki beberapa kekurangan, salah satunya adalah dapat dimanipulasi. Maka dibutuhkan suatu cara lain untuk menentukan apakah sebuah ulasan dapat membantu calon pelanggan memutuskan pembelian produk. Penelitian ini bertujuan untuk membuat sebuah model pemeringkatan ulasan berdasarkan tingkat kegunaan ulasan hasil regresi. Metode yang digunakan pada penelitian ini adalah Random Forest Regression. Terdapat enam tahapan utama pada metodologi ini, dimulai dari pengumpulan data ulasan pelanggan, praproses data, ekstraksi aspek dan analisis sentiment aspek untuk menentukan polaritas aspek, pembuatan model regresi dan pemeringkatan, dan analisis hasil. Hasil penelitian menunjukkan bahwa model pemeringkatan yang dibuat berdasarkan hasil regresi mempunyai kinerja yang lebih unggul dibandingkan dengan model yang dibuat hanya berdasarkan nilai helpfulness ratio saja. Hal ini dibuktikan dengan model tersebut unggul pada pengujian nilai kecocokan yang dilakukan dengan peningkatan kinerja sebesar 6%.

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HIGUCHI, Yoshiyuki, Yuki WATANABE, Takako UCHIDA, and Takayuki SUZUKI. "Prediction Model of Dementia Wandering by Random Forest Regression." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2021 (2021): IIP2B2–5. http://dx.doi.org/10.1299/jsmeiip.2021.iip2b2-5.

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27

Chen, Yihui, and Minjie Li. "Evaluation of influencing factors on tea production based on random forest regression and mean impact value." Agricultural Economics (Zemědělská ekonomika) 65, No. 7 (July 17, 2019): 340–47. http://dx.doi.org/10.17221/399/2018-agricecon.

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Overproduction of tea in the major producing countries is an important factor which restricts the development of tea. Therefore, the factors from the economic, social and environmental system affecting tea production have become the focus of both academia and practice. Random forest regression (RFR) and mean impact value (MIV) were applied to evaluate the weights of variables. Firstly, RFR was preliminarily used to build a well-trained model, and then the weights of variables combining with MIV were calculated. Then, a well-trained model was constructed after variable selection to evaluate the importance of tea production from 2007 to 2016. The results revealed that the economic system and the social system are the main factors that affect tea production. The net production value and total population have little negative effects on tea production, while the area harvested has a little positive effect. Based on the research findings, governments and enterprises should develop and upgrade tea production technology, promote the exchange and cooperation in the international tea trade, then ultimately achieve sustainable development of the tea industry.

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Roy, Marie-Hélène, and Denis Larocque. "Prediction intervals with random forests." Statistical Methods in Medical Research 29, no. 1 (February 21, 2019): 205–29. http://dx.doi.org/10.1177/0962280219829885.

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The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the classification and regression tree (CART) paradigm and the transformation forest method. For CART forests, in addition to the default least-squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated, and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data.

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Schonlau, Matthias, and Rosie Yuyan Zou. "The random forest algorithm for statistical learning." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (March 2020): 3–29. http://dx.doi.org/10.1177/1536867x20909688.

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Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest. We overview the random forest algorithm and illustrate its use with two examples: The first example is a classification problem that predicts whether a credit card holder will default on his or her debt. The second example is a regression problem that predicts the logscaled number of shares of online news articles. We conclude with a discussion that summarizes key points demonstrated in the examples.

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G, Manoj Kumar. "Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction." Revista Gestão Inovação e Tecnologias 11, no. 4 (July 10, 2021): 1228–40. http://dx.doi.org/10.47059/revistageintec.v11i4.2182.

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Nagpal, Arpita, and Vijendra Singh. "Coupling Multivariate Adaptive Regression Spline (MARS) and Random Forest (RF)." International Journal of Healthcare Information Systems and Informatics 14, no. 1 (January 2019): 1–18. http://dx.doi.org/10.4018/ijhisi.2019010101.

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In this article, a new algorithm to select the relevant features is proposed for handling microarray data with the specific aim of increasing classification accuracy. In particular, the optimal genes are extracted using filter and wrapper feature selection algorithms. Here, the use of non-parametric regression algorithm called Multivariate Adaptive Regression Spline (MARS) followed by proposed Random Forest Statistical Test (RFST) algorithm are being studied. The study evaluates the comparative performance of the results of RFST and MARS with existing algorithms on ten standard microarray datasets. For performance analysis, three parameters are taken into consideration, namely, the number of selected features, runtime, and classification accuracy. Experimental results indicate that different feature selection algorithms yield different candidate gene subset; therefore, a Hybrid approach is applied to determine the best candidate genes to provide maximum information about the disease. The findings foretell that the RFST is performing better on six out of ten datasets whereas MARS is performing better on other datasets.

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Zahedi, Peyman, Saeid Parvandeh, Alireza Asgharpour, Brenton S. McLaury, Siamack A. Shirazi, and Brett A. McKinney. "Random forest regression prediction of solid particle Erosion in elbows." Powder Technology 338 (October 2018): 983–92. http://dx.doi.org/10.1016/j.powtec.2018.07.055.

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Liu, Na, Yanzhu Hu, and Xinbo Ai. "Research on Power Load Forecasting Based on Random Forest Regression." IOP Conference Series: Earth and Environmental Science 252 (July 9, 2019): 032171. http://dx.doi.org/10.1088/1755-1315/252/3/032171.

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Song, Jongwoo. "Bias corrections for Random Forest in regression using residual rotation." Journal of the Korean Statistical Society 44, no. 2 (June 2015): 321–26. http://dx.doi.org/10.1016/j.jkss.2015.01.003.

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Tang, Diane. "HEART DISEASE PREDICTION WITH LOGISTIC REGRESSION AND RANDOM FOREST MODEL." European Journal of Biomedical and Life Sciences, no. 1-2 (2021): 24–33. http://dx.doi.org/10.29013/elbls-21-1.2-24-33.

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Liu, Jian Ming, and Ji Guo Zeng. "Head Pose Estimation via Direction-Sensitive Feature and Random Regression Forests." Applied Mechanics and Materials 519-520 (February 2014): 693–96. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.693.

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Estimating the head pose is still a unique challenge for computer vision system. Previous methods at solving this problem have often proposed solutions formulated in a classification setting. In this paper, we formulate pose estimation as a regression problem to achieve robustness. We propose to use gradient orientation histograms based random regression forests for the task. Firstly, each sample image is divided into overlapped patches, and direction-sensitive features of patches are extracted. Then we train a random regression forest on these patches. Experiments are carried out on public available database, and the result shows that the proposed algorithm outperforms some other approaches in both accuracy and computational efficiency.

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Saraf, Samkit. "House Price Prediction Using Linear Regression." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1811–15. http://dx.doi.org/10.22214/ijraset.2021.38715.

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Guo, Futao, Guangyu Wang, Zhangwen Su, Huiling Liang, Wenhui Wang, Fangfang Lin, and Aiqin Liu. "What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests." International Journal of Wildland Fire 25, no. 5 (2016): 505. http://dx.doi.org/10.1071/wf15121.

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We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

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Egelberg, Jacob, Nina Pena, Rachel Rivera, and Christina Andruk. "Assessing the geographic specificity of pH prediction by classification and regression trees." PLOS ONE 16, no. 8 (August 11, 2021): e0255119. http://dx.doi.org/10.1371/journal.pone.0255119.

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Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The current study collected soil pH, climatic, and topographic data from 100 locations across New York’s Temperate Deciduous Forests (in the United States of America) to investigate the extrapolative capacity of a previously developed CART model as compared to novel CART and random forest (RF) models. Results showed that the previously developed CART underperformed in terms of predictive accuracy (RRMSE = 14.52%) when compared to a novel tree (RRMSE = 9.33%), and that a novel random forest outperformed both models (RRMSE = 8.88%), though its predictions did not differ significantly from the novel tree (p = 0.26). The most important predictors for model construction were climatic factors. These findings confirm existing reports that CART models are constrained by the spatial autocorrelation of geographic data and encourage the restricted application of relevant machine learning models to regions from which training data was collected. They also contradict previous literature implying that random forests should meaningfully boost the predictive accuracy of CARTs in the context of soil pH.

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Wang, Fangyi, Yongchao Wang, Xiaokang Ji, and Zhiping Wang. "Effective Macrosomia Prediction Using Random Forest Algorithm." International Journal of Environmental Research and Public Health 19, no. 6 (March 10, 2022): 3245. http://dx.doi.org/10.3390/ijerph19063245.

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(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

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Smith, Paul F., Siva Ganesh, and Ping Liu. "A comparison of random forest regression and multiple linear regression for prediction in neuroscience." Journal of Neuroscience Methods 220, no. 1 (October 2013): 85–91. http://dx.doi.org/10.1016/j.jneumeth.2013.08.024.

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Balla, Imanuel, Sri Rahayu, and Jajang Jaya Purnama. "GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST." Jurnal Techno Nusa Mandiri 18, no. 1 (March 15, 2021): 49–54. http://dx.doi.org/10.33480/techno.v18i1.2210.

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Clothing also means clothing is needed by humans. Besides the need for clothing in terms of function, clothing sales or business is also very potent. About 75 million people worldwide are directly involved in textiles, clothing, and footwear. In this case, a common problem in this industry is that the actual productivity of apparel employees sometimes fails to reach the productivity targets set by the authorities to meet production targets on time, resulting in huge losses. Experiments were conducted using the random forest model, linear regression, and neural network by looking for the values of the correlation coefficient, MAE, and RMSE. This aims to predict the productivity of garment employees with data mining techniques that apply machine learning and look for the minimum MAE value. The results of testing the proposed algorithm on the garment worker productivity dataset obtained the smallest MAE, namely the random forest algorithm, namely 0.0787, linear regression 0.1081, and 0.1218 neural networks

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Zhang, J., S. Huang, E. H. Hogg, V. Lieffers, Y. Qin, and F. He. "Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data." Biogeosciences 11, no. 10 (May 27, 2014): 2793–808. http://dx.doi.org/10.5194/bg-11-2793-2014.

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Abstract. Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present aboveground biomass (AGB) in Alberta, Canada, by taking advantage of a spatially explicit data set derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26 × 109 Mg (megagram), with an average AGB density of 56.30 ± 35.94 Mg ha−1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39 × 109 Mg biomass, accounting for nearly 62% of total estimated AGB. Spatial distribution of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne lidar data, land cover classification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.

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Zhang, J., S. Huang, E. H. Hogg, V. Lieffers, Y. Qin, and F. He. "Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data." Biogeosciences Discussions 10, no. 12 (December 4, 2013): 19005–44. http://dx.doi.org/10.5194/bgd-10-19005-2013.

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Abstract. Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present biomass carbon storage in Alberta, Canada, by taking advantage of a spatially explicit dataset derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (LiDAR) canopy height data. Ten climatic variables together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree based modelling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total biomass stock in Alberta forests was estimated to be 3.11 × 109 Mg, with the average biomass density of 77.59 Mg ha−1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.91 × 109 Mg biomass, accounting for 61% of total estimated biomass. Spatial distribution of biomass varied with natural regions, land cover types, and species. And the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne LiDAR data, land cover classification, climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.

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Shabani, Saeid, and Moslem Akbarinia. "PREDICTION SPATIAL PATTERNS OF WINDTHROW PHENOMENON IN DECIDUOUS TEMPERATE FORESTS USING LOGISTIC REGRESSION AND RANDOM FOREST." CERNE 23, no. 3 (September 2017): 387–94. http://dx.doi.org/10.1590/01047760201723032377.

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ABSTRACT Forest management needs to evaluate various hazards where may cause economic or other losses to forest owners. The aim of this study is to prepare windthrow hazard maps based on logistic regression and random forest models in Nowshahr Forests, Mazandaran Province, Iran. First of all, 200 windthrow locations were identified from extensive field surveys and some reports. Out these, 140 (70%) locations were randomly selected as training data and the remaining 60 (30%) cases were used for the validation goals. In the next step, 10 predictive variables such as slope degree, slope aspect, altitude, Topographic Position Index (TPI), Topographic Wetness Index (TWI), distance to roads and skid trails, wind effect, soil texture, forest type and stand density were extracted from the spatial database. Subsequently, windthrow hazard maps were produced using logistic regression and RF models, and the results were plotted in ArcGIS. Finally, the area under the curves (AUC) and kappa coefficient were made for performance purposes. The validation of results presented that the area under the curve and kappa have a more accuracy for the random forest (97.5%, and 95%, respectively) than logistic regression (96.667%, and 93.333%, respectively) model. Therefore, this technique has more potentiality to be applied in the evaluation of windthrow phenomenon in forest ecosystems. Additionally, both models indicate that the spatial distribution of windthrow incidence likelihood is highly variable in this region. In general, the mentioned findings can be applied for management of future windthrow in favor of economic benefits and environmental preservation.

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Luo, Mi, Yifu Wang, Yunhong Xie, Lai Zhou, Jingjing Qiao, Siyu Qiu, and Yujun Sun. "Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass." Forests 12, no. 2 (February 13, 2021): 216. http://dx.doi.org/10.3390/f12020216.

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Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

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Wang, Lei, Qingjian Zhao, Zuomin Wen, and Jiaming Qu. "RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression." Sustainability 10, no. 12 (December 5, 2018): 4620. http://dx.doi.org/10.3390/su10124620.

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Forest fire prevention is important because of human communities near forests or in the wildland-urban interfaces. Short-term forest fire danger rating prediction is an effective way to provide early guidance for forest fire managers. It can therefore effectively protect the forest resources and enhance the sustainability of the forest ecosystem. However, relevant existing forest fire danger rating prediction models operate well only when applied to distinct climates and fuel types separately. There are desires for an effective methodology, which can construct a specific short-term prediction model according to an evaluation of the data from that specific region. Moreover, a suitable method for prediction model construction needs to deal with some big data related computing challenges (i.e., data diversity coupled with complexity of solution space, and the requirement of real-time forest fire prevention application) when massively observed heterogeneous parameters are available for prediction (e.g., meteorology factor, the amount of litter in the area, soil moisture, etc.). To capture the influences of multiple prediction factors on the prediction results and effectively learn from fast cumulative historical big data, artificial intelligence methods are investigated in this paper, yielding a short-term Ratings of Forest Fire Danger Prediction via Multiclass Logistic Regression (or RAFFIA) model for forest fire danger rating online prediction. Experimental evaluations conducted on a sensor-based forest fire prevention experimental station show that RAFFIA (with 98.71% precision and 0.081 root mean square error) is more effective than the Least Square Fitting Regression (LSFR) and Random Forests (RF) prediction models.

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Melišová, Eva, Adam Vizina, Martin Hanel, Petr Pavlík, and Petra Šuhájková. "Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic." Hydrology 8, no. 4 (October 12, 2021): 153. http://dx.doi.org/10.3390/hydrology8040153.

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Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.

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Melišová, Eva, Adam Vizina, Martin Hanel, Petr Pavlík, and Petra Šuhájková. "Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic." Hydrology 8, no. 4 (October 12, 2021): 153. http://dx.doi.org/10.3390/hydrology8040153.

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Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.

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Hutengs, Christopher, and Michael Vohland. "Downscaling land surface temperatures at regional scales with random forest regression." Remote Sensing of Environment 178 (June 2016): 127–41. http://dx.doi.org/10.1016/j.rse.2016.03.006.

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