article · Sustainability
Groundwater quality assessment in the Ghiss-Nekkor aquifer near Al Hoceima, Morocco, was conducted using fifty samples collected in May 2019. Testing covered key physicochemical parameters, including electrical conductivity, total dissolved solids, pH, and major ions. These parameters informed an entropy-weighted groundwater quality index to assess drinking suitability. The resulting index values ranged between 90.98 and 337.28, with high electrical conductivity and chloride levels linked directly to degraded quality. Most tested parameter values exceeded World Health Organization drinking guidelines, with one sample categorised as completely unsuitable due to seawater intrusion, overexploitation, and severe weather. To streamline evaluation, a multilayer perceptron neural network was developed to predict water quality using inputs of mineralization, total hardness, nitrate, and nitrite. The predictive model achieved a coefficient of determination of 0.9885 and demonstrated high accuracy in validation testing.
Groundwater is a vital resource for communities, but coastal aquifers face severe pressures from overexploitation and seawater intrusion. Accurately assessing drinking suitability traditionally requires extensive laboratory testing. Developing reliable artificial intelligence models that predict water quality indices from fewer chemical indicators offers a faster, more practical method for monitoring contamination, helping protect public health and identify areas where water supplies exceed safety limits.
The developed neural network model demonstrates an applied and tested approach for water utility operators, environmental regulators, and municipal authorities seeking rapid groundwater assessment. By predicting water quality indexes from a reduced set of inputs including mineralization, hardness, and nitrogen compounds, the tool could be integrated into software for routine watershed monitoring. However, deployment remains at an applied research stage, requiring translation into user-friendly monitoring platforms and validation across other regional aquifers.
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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%.
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DOI: 10.3390/su15010402
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