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Analysis of different combinations of meteorological parameters and well characteristics in predicting the groundwater chloride concentration with different empirical approaches: a case study in Gaza Strip, Palestine

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Abstract

The main aim of the present study was to select a better model for predicting the groundwater salinity in the Gaza Strip, Palestine for the first time. The purpose of this work was to identify the important parameters that affect the prediction of groundwater salinity in the selected region using various empirical models. In this study, groundwater salinity is expressed in terms of chloride concentration. Accordingly, 255 MLPNN (Multi-layer perceptron neural network) models were developed to find the most important parameters influencing the prediction of chloride concentration. The parameters include recharge rate (RR), abstraction average rate (AVR), groundwater level (GWL), distance from sea shoreline (DSSL), rainfall (R), the difference between the maximum and minimum temperature (DT), and relative humidity (RH) in addition to initial chloride concentration (ICC). The results indicated that MLPNN#166 with the combination of [ICC RR AVR GWL DT] and MLPNN#1 with the combination of [ICC R] have shown a high prediction accuracy based on the value of statistical measures. Second, out of 255 MLPNN models, the best 17 MLPNN models were selected and their performance was compared with Radial Basis Neural Networks (RBFNN), Quadratic model (QM), and multiple linear regression (MLR) models using various statistical measures. The results showed that the QM model with the combination of [ICC RR AVR GWL R RH DT] performed better than MLPNN, RBFNN, and MLR. Finally, two ensemble techniques were utilized to increase the prediction capabilities of MLPNN and RBFNN models when compared with single prediction models. To achieve this, 20 scenarios are proposed to identify the best hybrid model. The results showed that MLPNN-QM, MLPNN-QM-MLR, and MLPNN-RBFNN-QM-MLR were considered to be the best prediction model. Among of 24 developed approaches, the results reported that the QM model is the most superior model for predicting the groundwater chloride concentration.

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Data availability statement

The manuscript has no associated data to provide. However, additional information has been provided as supplementary material.

Notes

  1. See Ref. (Barati-Harooni and Najafi-Marghmaleki 2016).

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Acknowledgements

The author would like to thank the Faculty of Engineering, especially the Mechanical Engineering Department, at Near East University for their support and encouragement.

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  1. Department of Mechanical Engineering, Engineering Faculty, Near East University (Via Mersin 10, Turkey), 99138, Nicosia, Cyprus

    Youssef Kassem

  2. Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University (Via Mersin 10, Turkey), 99138, Nicosia, Cyprus

    Youssef Kassem

  3. Energy, Environment, and Water Research Center, Near East University (Via Mersin 10, Turkey), 99138, Nicosia, Cyprus

    Youssef Kassem

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  1. Youssef Kassem
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The author contributed to the work. YK collected the data and analyzed them. YK wrote the paper. The author has read and agreed to the published version of the manuscript.

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Kassem, Y. Analysis of different combinations of meteorological parameters and well characteristics in predicting the groundwater chloride concentration with different empirical approaches: a case study in Gaza Strip, Palestine. Environ Earth Sci 82, 134 (2023). https://doi.org/10.1007/s12665-023-10767-9

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