Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment

Ha Duong Hai; Huong Thi Thanh Ngo; Phong Tran Van; Dam Nguyen Duc; Mohammadtaghi  Avand; Duy Nguyen Huu; Mahdis  Amiri; Hiep Van Le; Indra  Prakash; Pham Binh Thai

doi:10.15625/2615-9783/17240

Author affiliations

Authors

Duong Hai Ha Institute for Water and Environment, Hanoi 100000, Vietnam
Huong Thi Thanh Ngo University of Transport Technology, Hanoi 100000, Vietnam
Tran Van Phong Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, Hanoi, Vietnam
Nguyen Duc Dam University of Transport Technology, Hanoi 100000, Vietnam
Mohammadtaghi Avand Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran 14115-111, Iran
Huu Duy Nguyen Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam
Mahdis Amiri Gorgan University of Agricultural Sciences & Natural Resources, Department of Watershed & Arid
Hiep Van Le University of Transport Technology, Hanoi 100000, Vietnam
Indra Prakash Dy. Director General (R), Geological Survey of India, Gandhinagar (Guj.) 382002, India
Binh Thai Pham University of Transport Technology, Hanoi 100000, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/17240

Keywords:

Groundwater potential mapping, Random Forest, artificial intelligence, hybrid models, Vietnam

Abstract

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones.

One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the Average Merit (AM) of the rainfall factor was the highest (68.039), and river density was the lowest (53,969). Performance evaluation of ML models was done using standard statistical indicators, including Area Under the Receiver Operating Characteristic (ROC) curve (AUC). The results showed that all the four models performed well in the training (AUC ≥ 0.967) and testing (AUC ≥ 0.734) phases, but the performance of the ABRF (AUC=0.992) model is the best in the training phase, whereas LBRF is the best in the testing phase (AUC=0.776). The present model study would be helpful in the proper groundwater potential assessment and management of groundwater resources for sustainable development.

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