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Groundwater level prediction using machine learning algorithms in a drought-prone area

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Abstract

Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life, and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in the sustainable management of GWR. A comparative study was conducted to evaluate the performance of seven different ML models, such as random tree (RT), random forest (RF), decision stump, M5P, support vector machine (SVM), locally weighted linear regression (LWLR), and reduce error pruning tree (REP Tree) for GW level (GWL) prediction. The long-term prediction was conducted using historical GWL, mean temperature, rainfall, and relative humidity datasets for the period 1981–2017 obtained from two wells in the northwestern region of Bangladesh. The whole dataset was divided into training (1981–2008) and testing (2008–2017) datasets. The output of the seven proposed models was evaluated using the root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), correlation coefficient (CC), and Taylor diagram. The results revealed that the Bagging-RT and Bagging-RF models outperformed other ML models. The Bagging-RT models can effectively improve prediction precision as compared to other models with RMSE of 0.60 m, MAE of 0.45 m, RAE of 27.47%, RRSE of 30.79%, and CC of 0.96 for Rajshahi and RMSE of 0.26 m, MAE of 0.18 m, RAE of 19.87%, RRSE of 24.17%, and 0.97 for Rangpur during training, and RMSE of 0.60 m, MAE of 0.40 m, RAE of 24.25%, RRSE of 29.99%, and CC of 0.96 for Rajshahi and RMSE of 0.38 m, MAE of 0.24 m, RAE of 23.55%, RRSE of 31.77%, and CC of 0.95 for Rangpur during testing stages, respectively. Our study offers an effective and practical approach to the forecast of GWL that could help to formulate policies for sustainable GWR management.

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Availability of data and materials

The data that support the findings of this study are available from the first author, [Quoc Bao Pham, phambaoquoc@tdmu.edu.vn], upon reasonable request.

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Authors and Affiliations

  1. Institute of Applied Technology, Thu Dau Mot University, Binh Duong Province, Vietnam

    Quoc Bao Pham

  2. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia, Będzińska Street 60, 41-200, Katowice, Sosnowiec, Poland

    Quoc Bao Pham

  3. Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, 263145, India

    Manish Kumar

  4. Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, DICEM, Via Di Biasio, 43, 03043, Cassino, Frosinone, Italy

    Fabio Di Nunno & Francesco Granata

  5. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

  6. Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh

    Abu Reza Md. Towfiqul Islam

  7. Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, Delhi, India

    Swapan Talukdar

  8. Center for Advanced Chemistry, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    X. Cuong Nguyen

  9. Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    X. Cuong Nguyen

  10. Institute of Energy Infrastructure (IEI), Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional(UNITEN), Kajang, 43000, Selangor, Malaysia

    Ali Najah Ahmed

  11. HUTECH University, 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam

    Duong Tran Anh

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  10. Duong Tran Anh
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Contributions

QBP contributed to project administration, conceptualization, writing—original draft, formal analysis, visualization. AE contributed to software, formal analysis, writing—original draft, visualization. MK, FDN, FG, ARMTI, ST contributed to writing, review and editing. XCN, ANA, DTA contributed to supervision, writing, review, editing.

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Correspondence to Duong Tran Anh.

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Pham, Q.B., Kumar, M., Di Nunno, F. et al. Groundwater level prediction using machine learning algorithms in a drought-prone area. Neural Comput & Applic 34, 10751–10773 (2022). https://doi.org/10.1007/s00521-022-07009-7

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