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Open AccessArticle

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

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Vietnam Academy for Water Resources, Hanoi 100000, Vietnam
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Institute for Water and Environment, Hanoi 100000, Vietnam
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Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), P.O. Box 64414-356 Tehran, Iran
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Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi 100000, Vietnam
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Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
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Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
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Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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University of Transport Technology, Hanoi 100000, Vietnam
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Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(7), 2473; https://doi.org/10.3390/ijerph17072473
Received: 2 March 2020 / Revised: 31 March 2020 / Accepted: 3 April 2020 / Published: 4 April 2020
(This article belongs to the Special Issue Water Resources and Hydro-Ecology)
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. View Full-Text
Keywords: groundwater potential mapping; ensemble modeling; spatial modeling; machine learning groundwater potential mapping; ensemble modeling; spatial modeling; machine learning
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Nguyen, P.T.; Ha, D.H.; Jaafari, A.; Nguyen, H.D.; Van Phong, T.; Al-Ansari, N.; Prakash, I.; Le, H.V.; Pham, B.T. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam. Int. J. Environ. Res. Public Health 2020, 17, 2473.

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