No 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%.

Soil erosion is an increasingly issue worldwide, due to several factors including climate variations and humans’ activities, especially in Mediterranean ecosystems. Therefore, the aim of this paper is: (i) to quantify and to predict soil erosion rate for the baseline period (2000–2013) and a future period (2014–2027), using the Revised Universal Soil Loss Equation (RUSLE) and the Soil and Water Assessment Tool (SWAT) model in the R’Dom watershed in Morocco, based on the opportunities of Remote Sensing (RS) techniques and Geographical Information System (GIS) geospatial tools. (ii) we based on classical statistical downscaling model (SDSM) for rainfall prediction. Due to the lack of field data, the model results are validated by expert knowledge. As a result of this study, it is found that both agricultural lands and bare lands are most affected by soil erosion. Moreover, it is showed that soil erosion in the watershed was dominated by very low and low erosion. Although the area of very low erosion and low erosion continued to decrease. Hence, we hereby envisage that our contribution will provide a more complete understanding of the soil degradation in this study area and the results of this research could be a crucial reference in soil erosion studies and also may serve as a valuable guidance for watershed management strategies.

Water is one of the fundamental resources of economic prosperity, food security, human habitats, and the driver of many global phenomena, such as droughts, floods, contaminated water, disease, poverty, and hunger. Therefore, its deterioration and its inadequate use lead to heavy impacts on environmental resources and humans. Thus, we argue that to address these challenges, one can rely on hydrological management strategies. The objective of this study is to simulate and quantify water balance components based on a hydrologic model with available data at the R’Dom watershed in Morocco. For this purpose, the hydrologic model used is the Soil and Water Assessment Tool + (SWAT+) model. The streamflow model simulations were run at the monthly time step (from 2002 to 2016), during the calibration period 2002–2009, the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) values were 0.84 and 0.70, respectively, and 0.81 and 0.65, respectively, during the validation period 2010–2016. The results of the water balance modeling in the watershed during the validation period revealed that the average annual precipitation was about 484 mm, and out of this, 5.75 mm came from the development of irrigation in agricultural lands. The evapotranspiration accounted for about 72.28% of the input water of the watershed, while surface runoff (surq_gen) accounted for 12.04%, 11.90% was lost by lateral flow (latq), and 4.14% was lost by groundwater recharge (perco). Our approach is designed to capture a real image of a case study; zooming into other case studies with similar environments to uncover the situation of water resources is highly recommended. Moreover, the outcomes of this study will be helpful for policy and decision-makers, and it can be a good path for researchers for further directions based on the SWAT model to simulate water balance to achieve adequate management of water resources.