Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

Bien Tran Xuan; Trinh Pham The; Duong Luu Thuy; Phong Tran Van; Nhat Vuong Hong; Hiep Van Le; Dam Duc Nguyen; Indra Prakash; Tam Pham Thanh; Binh Pham Thai

doi:10.15625/2615-9783/20316

Author affiliations

Authors

Tran Xuan Bien Hanoi University of Natural Resources and Environment, Hanoi, Vietnam
Pham The Trinh 1-Tay Nguyen University, 567 Le Duan, Buon Ma Thuot, DakLak Province, Vietnam; 2-Department of Science and Technology, DakLak Province, Vietnam
Luu Thuy Duong Hanoi University of Natural Resources and Environment, Hanoi, Vietnam
Tran Van Phong 1-Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, Vietnam; 2-Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
Vuong Hong Nhat Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam
Hiep Van Le University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam
Dam Duc Nguyen University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam
Indra Prakash DDG(R) Geological Survey of India, Gandhinagar, Gujarat 382010, India
Pham Thanh Tam Thai Nguyen University of Agriculture and Forestry, Thai Nguyen, Vietnam
Binh Thai Pham University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/20316

Keywords:

: Machine learning, groundwater potential mapping, Logistics Model Trees, cascade generalization, Vietnam

Abstract

In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.

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