Using Boosted Regression Tree, Logistic Model Tree, and Random Forest Algorithms to Evaluate the Groundwater Potential

Document Type : Research

Authors

1 Master of Science (M.Sc.), Department of Surveying Engineering, Faculty of Surveying Engineering and Geospatial Information, University of Tehran, Tehran, Iran

2 Master of Science (M.Sc.), Civil Engineering, Water and Hydraulic Structures, Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran

3 Associate Professor of Civil Engineering Department, University of Birjand

Abstract

Groundwater is exploited uncontrollably due to population growth and industrialization in different parts of the world. The purpose of this study is to evaluate the groundwater potential by advanced machine learning algorithms using topographical, hydrological, environmental, and geological criteria. To do this, three advanced machine learning algorithms were used, including Boosted Regression Tree (BRT), Logistic Model Tree (LMT), and Random Forest (RF). Therefore, for implementation, geo-hydrological data of 37 groundwater wells in Birjand plain of South Khorasan province were collected and randomly selected in a ratio of 70 to 30 were divided into training and validation data sets. Finally, groundwater potential maps were prepared using BRT, LMT, and RF algorithms. In order to validate the groundwater potential prediction algorithms, the area under the curve (AUC) and the statistical criteria of positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy were used. The results showed that the LMT model (AUC = 0.865) has a better performance than the BRT and RF models in predicting the groundwater potential of the study area.

Keywords

References

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Watershed Management Research
Volume 35, Issue 3 - Serial Number 136
September 2022
Pages 44-59

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