Assessment and Prediction of the Water Quality Index for the Groundwater of the Ghiss-Nekkor (Al Hoceima, Northeastern Morocco)

Yassine El Yousfi, Mahjoub Himi, Hossain El Ouarghi, Mourad Aqnouy, Said Benyoussef, Hicham Gueddari, Hanane Ait Hmeid, Abdennabi Alitane (), Mohamed Chaibi, Muhammad Zahid, Narjisse Essahlaoui, Sliman Hitouri, Ali Essahlaoui and Abdallah Elaaraj
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Yassine El Yousfi: Environmental Management and Civil Engineering Team, ENSAH, Abdelmalek Essaadi University, Tetouan 93030, Morocco
Mahjoub Himi: Environmental Management and Civil Engineering Team, ENSAH, Abdelmalek Essaadi University, Tetouan 93030, Morocco
Hossain El Ouarghi: Environmental Management and Civil Engineering Team, ENSAH, Abdelmalek Essaadi University, Tetouan 93030, Morocco
Mourad Aqnouy: Applied Geology and Remote Sensing Research Team, Applied Geology Research Laboratory, Faculty of Sciences and Techniques, Moulay Ismaïl University of Meknes, Errachidia 52000, Morocco
Said Benyoussef: Environmental Management and Civil Engineering Team, ENSAH, Abdelmalek Essaadi University, Tetouan 93030, Morocco
Hicham Gueddari: OLMAN BPGE Laboratory, Multidisciplinary Faculty of Nador, Mohamed First University, Oujda 60000, Morocco
Hanane Ait Hmeid: OLMAN BPGE Laboratory, Multidisciplinary Faculty of Nador, Mohamed First University, Oujda 60000, Morocco
Abdennabi Alitane: Research Group “Water Sciences and Environment Engineering”, Geoengineering and Environment Laboratory, Geology Department, Faculty of Sciences, Moulay Ismail University, Presidency, Marjane 2, P.O. Box 298, Meknes 50050, Morocco
Mohamed Chaibi: Team of Renewable Energy and Energy Efficiency, Department of Physics, Faculty of Science, University of Moulay Ismail, Zitoune, P.O. Box 11201, Meknes 50050, Morocco
Muhammad Zahid: Key Laboratory of Functional Inorganic Material Chemistry, Ministry of Education of the People’s Republic of China, Heilongjiang University, Harbin 150080, China
Narjisse Essahlaoui: Research Group “Water Sciences and Environment Engineering”, Geoengineering and Environment Laboratory, Geology Department, Faculty of Sciences, Moulay Ismail University, Presidency, Marjane 2, P.O. Box 298, Meknes 50050, Morocco
Sliman Hitouri: Geosciences Laboratory, Department of Geology, Faculty of Sciences, University Ibn Tofail, P.O. Box 133, Kenitra 14000, Morocco
Ali Essahlaoui: Research Group “Water Sciences and Environment Engineering”, Geoengineering and Environment Laboratory, Geology Department, Faculty of Sciences, Moulay Ismail University, Presidency, Marjane 2, P.O. Box 298, Meknes 50050, Morocco
Abdallah Elaaraj: Natural Resources and Environment Laboratory, Geology Department, Polydisciplinary Faculty of Taza, Route d’Oujda, P.O. Box 1223, Taza 35000, Morocco

Sustainability, 2022, vol. 15, issue 1, 1-19

Abstract: Water quality index (WQI) is the primary method applied to characterize water quality in the world. The current study employed the statistical analysis and multilayer perceptron (MLP) approaches for predicting groundwater quality in the Ghiss-Nekkor aquifer, northeast of Al Hoceima, Morocco. Fifty sampled groundwater were identified and analyzed for major anions and cations throughout May 2019. Several physicochemical parameters of all the samples were identified in this investigation, such as TDS, pH, EC, Na, K, Ca, Mg, HCO 3 , NO 3 , Br, SO 4 , and Cl. The entropy-weighted groundwater quality index (EWQI) was calculated from these parameters. The WQI procedure determined the suitability of groundwater for consumption. The WQI value varied from 90.98 to 337.28. The EC, TDS, WQI, and Cl − spatial distribution showed that EC and Cl − are associated with poor groundwater quality. A single sample (W16) represented unsuitable water for drinking purposes and offered a WQI value of 337.28, indicating poor drinking quality due to seawater intrusion, overexploitation, and harsh weather conditions. The majority of the values obtained for the parameters exceeded the recommended limit of the World Health Organization (WHO)’s guidelines for consumption. The findings show that using parameters is a straightforward method for predicting water quality indexes with sufficient and suitable precision. The MLP model shows good predictive performances in terms of the coefficient of determination R 2 , mean absolute error (MAE), and root-mean-square error (RMSE) with values of 0.9885, 5.8031, and 4.7211, respectively. The ANN approach was applied to develop a model that can accurately predict WQI utilizing mineralization, TH, NO3, and NO2 as inputs. The MAE for the model’s performance was calculated to be 4.72. A Bland–Altman test was used to validate that the model is suitable. Following the test, it was determined that the model is appropriate for predicting WQI, with an error of just 0.1%.

Keywords: groundwater quality; prediction; water quality index; Ghiss-Nekkor; WHO; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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