Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling

Phong Tung Nguyen, Duong Hai Ha, Huu Duy Nguyen, Tran Van Phong, Phan Trong Trinh, Nadhir Al-Ansari, Hiep Van Le, Binh Thai Pham, Lanh Si Ho and Indra Prakash
Additional contact information
Phong Tung Nguyen: Vietnam Academy for Water Resources, Hanoi 100000, Vietnam
Duong Hai Ha: Institute for Water and Environment, Hanoi 100000, Vietnam
Huu Duy Nguyen: Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi 100000, Vietnam
Tran Van Phong: Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
Phan Trong Trinh: Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
Nadhir Al-Ansari: Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
Hiep Van Le: University of Transport Technology, Hanoi 100000, Vietnam
Binh Thai Pham: University of Transport Technology, Hanoi 100000, Vietnam
Lanh Si Ho: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Indra Prakash: Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India

Sustainability, 2020, vol. 12, issue 7, 1-28

Abstract: Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic ( AUC ) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT ( AUC = 0.770), BCDT ( AUC = 0.731), Dagging-CDT ( AUC = 0.763), Decorate-CDT ( AUC = 0.750), and RSSCDT ( AUC = 0.766) improved significantly in comparison to the single CDT ( AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.

Keywords: Groundwater potential mapping; Machine learning; Ensemble Frameworks; Vietnam (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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