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%.

Groundwater resources of the alluvial aquifer Ghiss Nekkor, which covers an area of 100 km², are the main source of domestic and agricultural freshwater supply in the region of Al Hoceima in Morocco. Due to human activities (overexploitation, increase in agricultural activity), this alluvial aquifer has become very sensitive to chemical pollution. The principal objective of this current study is to develop and implement a calibration method to assess, map, and estimate the vulnerability of the Ghiss Nekkor alluvial aquifer to pollution risk. In this work, the GIS-based DRASTIC model was used to estimate the inherent vulnerability to contamination of the Ghiss Nekkor alluvial aquifer with seven standard hydrogeological parameters. Nitrate (NO₃) and electrical conductivity (EC) data were used to validate the DRASTIC map. The results of the vulnerability map analysis show that the vulnerability to contaminants varies from non-existent in the southwestern part of the plain (7.3% of the total area), to very high (14.5%). The vulnerability is moderate in the central and northeastern areas (26.9%), while it is high in the other areas (17.5%). Furthermore, the most sensitive areas are mainly concentrated near the coastal strip and the central plain on both sides of the Nekkor River. In these areas, the NO₃ and EC values are above the maximum allowable limit of the World Health Organization. The results suggest that the DRASTIC model can be an effective tool for decision-makers concerned about managing groundwater sustainability.

Together with geological survey data, satellite imagery provides useful information for geological mapping. In this context, the aim of this study is to map geological units of the Saka region, situated in the northeast part of Morocco based on Landsat Oli-8 and ASTER images. Specifically, this study aims to: (1) map the lithological facies of the Saka volcanic zone, (2) discriminate the different minerals using Landsat Oli-8 and ASTER imagery, and (3) validate the results with field observations and geological maps. To do so, in this study we used different techniques to achieve the above objectives including color composition (CC), band ratio (BR), minimum noise fraction (MNF), principal component analysis (PCA), and spectral angle mapper (SAM) classification. The results obtained show good discrimination between the different lithological facies, which is confirmed by the supervised classification of the images and validated by field missions and the geological map with a scale of 1/500,000. The classification results show that the study area is dominated by Basaltic rocks, followed by Trachy andesites then Hawaites. These rocks are encased by quaternary sedimentary rocks and an abundance of Quartz, Feldspar, Pyroxene, and Amphibole minerals.

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