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Salinity Prediction in Coastal Aquifers of the Vietnamese Mekong River Delta Using Innovative Machine Learning Algorithms

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Advances in Research on Water Resources and Environmental Systems (GTER 2022)

Part of the book series: Environmental Science and Engineering ((ESE))

Abstract

Groundwater salinization is a severe issue, causing various problems to human health, agriculture, ecosystems, and infrastructure in many coastal regions across the world. However, this phenomenon is difficult to predict with high accuracy. In this study, we propose and verify a new artificial intelligence approach for predicting groundwater salinity and identifying the main factors of salinization. The coastal aquifers of the Mekong River Delta (Vietnam) were selected to test the new approach. In the proposed approach, Extreme Gradient Boosting (XGB) was used to build a groundwater salinity model, and Genetic Optimization (GO) was employed to optimize the model parameters. Gaussian Processes (GP) and Random Forests (RF) were also used as a benchmark for the model comparison. For this regard, a groundwater salinity database with 215 groundwater samples and 20 driven factors related to hydrology, geology, geography, and anthropogenic activities was prepared. Performance of the models was assessed using Correlation Coefficient (r), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results show that the proposed GO-XGB model yields high performance both on the training dataset (r = 0.999, RMSE = 18.450, MAPE = 2.070, and MAE = 4.864) and the validation dataset (r = 0.787, RMSE = 141.042, MAPE = 87.250, and MAE = 74.993). The proposed GO-XGB model performed better predictive result compared to the benchmark, GP, and RF. Among the 20 factors, groundwater level, vertical hydraulic conductivity, lithology, extraction capacity, horizontal hydraulic conductivity, distance to saline sources, and well density are the most important factors to groundwater salinization prediction.

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Authors and Affiliations

  1. Faculty of Water Resources Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Dang An Tran

  2. Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue, 530000, Vietnam

    Ha Nam Thang

  3. Department of Business and IT, USN School of Business, Campus Bø (1-324), Bø, Norway

    Dieu Tien Bui

  4. Department of Mine Surveying, Hanoi University of Mining and Geology, Hanoi, Vietnam

    Vuong Trong Kha

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  1. Dang An Tran
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  2. Ha Nam Thang
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  3. Dieu Tien Bui
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  4. Vuong Trong Kha
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Correspondence to Dang An Tran .

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Editors and Affiliations

  1. Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh (VNU-HCM), Ho Chi Minh City, Vietnam

    Phu Le Vo

  2. Faculty of Water Resources Engineering, Thuyloi University, Hanoi, Vietnam

    Dang An Tran

  3. Hanoi University of Mining and Geology, Hanoi, Vietnam

    Thi Lan Pham

  4. Department of Mine Surveying Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi, Vietnam

    Ha Le Thi Thu

  5. Hanoi University of Mining and Geology, Hanoi, Vietnam

    Nghia Nguyen Viet

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Tran, D.A., Thang, H.N., Bui, D.T., Kha, V.T. (2023). Salinity Prediction in Coastal Aquifers of the Vietnamese Mekong River Delta Using Innovative Machine Learning Algorithms. In: Vo, P.L., Tran, D.A., Pham, T.L., Le Thi Thu, H., Nguyen Viet, N. (eds) Advances in Research on Water Resources and Environmental Systems. GTER 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-17808-5_25

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