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

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, HCO3, NO3, Br, SO4, 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 R2, 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%.

Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach

“…The sources of chloride in groundwater include rocks and geological weathering, domestic wastes, and agricultural inputs [ 61 , 62 ]. The chloride concentrations ranged from 0.00 to 186 mg/L with a 22.68 mg/L average, and from 0 to 195 mg/L with a 29.98 mg/L average were recorded in the wet and dry sampling periods, respectively.…”

Hydrochemical evolution and groundwater quality assessment of the Tinejdad-Touroug quaternary aquifer, South-East Morocco

“…In general, a higher index implies potentially higher levels of pollution or contamination, however, a lower index suggests improved water quality. Several studies related to the assessment of groundwater quality within the adoption of the WQI have been carried out in Morocco (Bahir et al, 2020;El Mountassir et al, 2020;Jamaa et al, 2020;Bahir et al, 2021a;Bahir et al, 2021b;Sarti et al, 2021;Bahir et al, 2022;Ouhamdouch et al, 2022;Rafik et al, 2022;Benaissa et al, 2023;Boudellah et al, 2023;El Moustaine et al, 2023;El Yousfi et al, 2023).…”

Impact of Hydrographic Network Organization on the Morphology of the Kert Basin: Analysis of Consequences

“…It determines water flow pathways, affecting drainage, river flow dynamics, and the formation of watersheds, with implications for water availability, terrain morphology, and biodiversity [10][11][12]. This research addresses the current knowledge gap by investigating how the specific organization of the hydrographic network in the Kert basin influences its morphology, aiming to provide crucial insights for sustainable water resource management and ecosystem preservation [13][14][15]. Academically, this study aims to address two main objectives.…”