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Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Author

Listed:
  • Mohammed Benaafi

    (Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Mohamed A. Yassin

    (Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • A. G. Usman

    (Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, Nicosia 99138, Turkey)

  • S. I. Abba

    (Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca 2+ , Cl − , Br − , NO3 − , and Fe, and Na + , SO 4 2− , Mg 2+ , and F − combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10 −11 , 1.14 × 10 −14 , and MSE = 7.52 × 10 −2 , 3.88 × 10 −11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

Suggested Citation

  • Mohammed Benaafi & Mohamed A. Yassin & A. G. Usman & S. I. Abba, 2022. "Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2250-:d:750656
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    References listed on IDEAS

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    1. Zaher Mundher Yaseen & Hossam Faris & Nadhir Al-Ansari, 2020. "Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application," Complexity, Hindawi, vol. 2020, pages 1-14, February.
    2. Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.
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    Cited by:

    1. Rana Muhammad Adnan & Sarita Gajbhiye Meshram & Reham R. Mostafa & Abu Reza Md. Towfiqul Islam & S. I. Abba & Francis Andorful & Zhihuan Chen, 2023. "Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-29, March.

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