Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

Seyed Amir Naghibi; Kourosh Ahmadi; Alireza Daneshi

doi:10.1007/s11269-017-1660-3

Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

Water Resources Management ◽

10.1007/s11269-017-1660-3 ◽

2017 ◽

Vol 31 (9) ◽

pp. 2761-2775 ◽

Cited By ~ 103

Author(s):

Seyed Amir Naghibi ◽

Kourosh Ahmadi ◽

Alireza Daneshi

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Random Forest ◽

Groundwater Potential ◽

Support Vector ◽

Forest Models ◽

Random Forest Models ◽

Potential Mapping ◽

Groundwater Potential Mapping

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GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

Environmental Earth Sciences ◽

10.1007/s12665-016-5424-9 ◽

2016 ◽

Vol 75 (8) ◽

Cited By ~ 80

Author(s):

Mohsen Zabihi ◽

Hamid Reza Pourghasemi ◽

Zohre Sadat Pourtaghi ◽

Morteza Behzadfar

Keyword(s):

Random Forest ◽

Groundwater Potential ◽

Multivariate Adaptive Regression Spline ◽

Regression Spline ◽

Forest Models ◽

Random Forest Models ◽

Potential Mapping ◽

Adaptive Regression ◽

Groundwater Potential Mapping

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GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea

Geocarto International ◽

10.1080/10106049.2017.1303091 ◽

2017 ◽

Vol 33 (8) ◽

pp. 847-861 ◽

Cited By ~ 38

Author(s):

Saro Lee ◽

Soo-Min Hong ◽

Hyung-Sup Jung

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Groundwater Potential ◽

Support Vector ◽

Potential Mapping ◽

Artificial Neural ◽

Groundwater Potential Mapping

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Fusion of Ultraviolet and Infrared Spectra Using Support Vector Machine and Random Forest Models for the Discrimination of Wild and Cultivated Mushrooms

Analytical Letters ◽

10.1080/00032719.2019.1692857 ◽

2019 ◽

Vol 53 (7) ◽

pp. 1019-1033

Author(s):

Sen Yao ◽

Jie-Qing Li ◽

Zhi-Li Duan ◽

Tao Li ◽

Yuan-Zhong Wang

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Infrared Spectra ◽

Support Vector ◽

Forest Models ◽

Random Forest Models ◽

Cultivated Mushrooms

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Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)

Journal of Hydrology ◽

10.1016/j.jhydrol.2020.125033 ◽

2020 ◽

Vol 588 ◽

pp. 125033 ◽

Cited By ~ 7

Author(s):

Mahdi Panahi ◽

Nitheshnirmal Sadhasivam ◽

Hamid Reza Pourghasemi ◽

Fatemeh Rezaie ◽

Saro Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Support Vector Regression ◽

Spatial Prediction ◽

Groundwater Potential ◽

Support Vector ◽

Potential Mapping ◽

Groundwater Potential Mapping

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A novel ensemble model of automatic multilayer perceptron, random forest, and ZeroR for groundwater potential mapping

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-09519-8 ◽

2021 ◽

Vol 193 (11) ◽

Author(s):

Shruti Sachdeva ◽

Bijendra Kumar

Keyword(s):

Random Forest ◽

Multilayer Perceptron ◽

Groundwater Potential ◽

Ensemble Model ◽

Potential Mapping ◽

Groundwater Potential Mapping

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Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran

CATENA ◽

10.1016/j.catena.2015.10.010 ◽

2016 ◽

Vol 137 ◽

pp. 360-372 ◽

Cited By ~ 192

Author(s):

Omid Rahmati ◽

Hamid Reza Pourghasemi ◽

Assefa M. Melesse

Keyword(s):

Random Forest ◽

Maximum Entropy ◽

Groundwater Potential ◽

Data Driven ◽

Maximum Entropy Models ◽

Potential Mapping ◽

Groundwater Potential Mapping ◽

Application Of Gis

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A method for handling metabonomics data from liquid chromatography/mass spectrometry: combinational use of support vector machine recursive feature elimination, genetic algorithm and random forest for feature selection

Metabolomics ◽

10.1007/s11306-011-0274-7 ◽

2011 ◽

Vol 7 (4) ◽

pp. 549-558 ◽

Cited By ~ 40

Author(s):

Xiaohui Lin ◽

Quancai Wang ◽

Peiyuan Yin ◽

Liang Tang ◽

Yexiong Tan ◽

...

Keyword(s):

Mass Spectrometry ◽

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Liquid Chromatography ◽

Random Forest ◽

Recursive Feature Elimination ◽

Support Vector ◽

Liquid Chromatography Mass Spectrometry ◽

Chromatography Mass Spectrometry

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Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-020-01891-0 ◽

2020 ◽

Cited By ~ 1

Author(s):

Shruti Sachdeva ◽

Bijendra Kumar

Keyword(s):

Random Forest ◽

Decision Trees ◽

Groundwater Potential ◽

Potential Mapping ◽

Boosted Decision Trees ◽

Groundwater Potential Mapping

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Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

Environmental Monitoring and Assessment ◽

10.1007/s10661-018-6507-8 ◽

2018 ◽

Vol 190 (3) ◽

Cited By ~ 67

Author(s):

Ali Golkarian ◽

Seyed Amir Naghibi ◽

Bahareh Kalantar ◽

Biswajeet Pradhan

Keyword(s):

Random Forest ◽

Groundwater Potential ◽

Multivariate Adaptive Regression Spline ◽

Regression Spline ◽

Spline Models ◽

Potential Mapping ◽

Adaptive Regression ◽

Groundwater Potential Mapping

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Groundwater potential mapping using hybridization of simulated annealing and random forest

Water Engineering Modeling and Mathematic Tools ◽

10.1016/b978-0-12-820644-7.00008-6 ◽

2021 ◽

pp. 391-403

Author(s):

Bahram Choubin ◽

Omid Rahmati

Keyword(s):

Simulated Annealing ◽

Random Forest ◽

Groundwater Potential ◽

Potential Mapping ◽

Groundwater Potential Mapping

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