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Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam

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Abstract

Groundwater resources are required for domestic water supply, agriculture, and industry, and the strategic importance of water resources will only increase in the context of climate change and population growth. For optimal management of this crucial resource, exploration of the potential of groundwater is necessary. To this end, the objective of this study was the development of a new method based on remote sensing, deep neural networks (DNNs), and the optimization algorithms Adam, Flower Pollination Algorithm (FPA), Artificial Ecosystem-based Optimization (AEO), Pathfinder Algorithm (PFA), African Vultures Optimization Algorithm (AVOA), and Whale Optimization Algorithm (WOA) to predict groundwater potential in the North Central region of Vietnam. 95 springs or wells with 13 conditioning factors were used as input data to the machine learning model to find the statistical relationships between the presence and nonpresence of groundwater and the conditioning factors. Statistical indices, namely root mean square error (RMSE), area under curve (AUC), accuracy, kappa (K) and coefficient of determination (R2), were used to validate the models. The results indicated that all the proposed models were effective in predicting groundwater potential, with AUC values of more than 0.95. Among the proposed models, the DNN-AVOA model was more effective than the other models, with an AUC value of 0.97 and an RMSE of 0.22. This was followed by DNN-PFA (AUC=0.97, RMSE=0.22), DNN-FPA (AUC=0.97, RMSE=0.24), DNN-AEO (AUC=0.96, RMSE=0.25), DNN-Adam (AUC=0.97, RMSE=0.28), and DNN-WOA (AUC=0.95, RMSE=0.3). In addition, according to the groundwater potential map, about 25–30% of the region was in the high and very high potential groundwater zone; 5–10% was in the moderate zone, and 60–70% was low or very low. The results of this study can be used in the management of water resources in general and the location of appropriate wells in particular.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request

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Authors and Affiliations

  1. Faculty of Geography, University of Science, Vietnam National University, Ha Noi, 334 Nguyen Trai, Thanh Xuan District, Hanoi, Vietnam

    Huu Duy Nguyen, Quan Vu Viet Du, Cong Tuan Nguyen, Quoc-Huy Nguyen & Quang-Thanh Bui

  2. Institute of Geography, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet Str., Cau Giay Dist., Hanoi, 100000, Vietnam

    Van Hong Nguyen

  3. Faculty of Geography, Graduate University of Science and Technology, 18 Hoang Quoc Viet Str., Cau Giay Dist., Hanoi, 100000, Vietnam

    Van Hong Nguyen

  4. Faculty of Hydrology, Meteorology, and Oceanography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan District, Hanoi, Vietnam

    Dinh Kha Dang

  5. Institute of Vietnamese Studies & Development Sciences, Vietnam National University (VNU), Hanoi, 10000, Vietnam

    Quang Hai Truong

  6. Faculty of Natural Sciences, Quy Nhon University, Quy Nhon, Khoa Khoa học tự nhiên - Trường Đại học, Quy Nhơn, Vietnam

    Ngo Bao Toan Dang

  7. Institute for Environment and Resources of Ho Chi Minh, 1 Mac Dinh Chi Str., 1 Dist, Ho Chi Minh, Vietnam

    Quang Tuan Tran

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  1. Huu Duy Nguyen
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Contributions

Huu Duy Nguyen, Quang Hai Truong, Quang-Thanh Bui : Conceptualization, Methodology. Huu Duy Nguyen, Quang Thanh Bui, Van Hong Nguyen, Quan Vu Viet Du, Cong Tuan Nguyen, Ngo Bao Toan Dang, Quang Tuan Tran, Quoc-Huy Nguyen, Dinh Kha Dang: Methodology, Material preparation, Validation, Analysis, Writing – original draft – Review & Editing, Writing – Review & Editing. Huu Duy Nguyen, Quoc Huy Nguyen: Data collection. All authors read and approved the final manuscript.

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Nguyen, H.D., Nguyen, V.H., Du, Q.V.V. et al. Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam. Earth Sci Inform 17, 1569–1589 (2024). https://doi.org/10.1007/s12145-023-01209-y

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