Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Phong Tung Nguyen, Duong Hai Ha, Abolfazl Jaafari, Huu Duy Nguyen, Tran Van Phong, Nadhir Al-Ansari, Indra Prakash, Hiep Van Le and Binh Thai Pham
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
Abolfazl Jaafari: Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), P.O. Box 64414-356 Tehran, Iran
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
Nadhir Al-Ansari: Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
Indra Prakash: Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India
Hiep Van Le: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Binh Thai Pham: University of Transport Technology, Hanoi 100000, Vietnam

IJERPH, 2020, vol. 17, issue 7, 1-20

Abstract: The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Keywords: groundwater potential mapping; ensemble modeling; spatial modeling; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (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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