Self-organizing maps (SOMs) and other artificial intelligence approaches developed by Kohonen can be used to model and solve environmental challenges. To emphasize the classification of Physico-chemical parameters of the Inaouen watershed, we presented a classification strategy based on a self-organizing topological map (SOM) artificial neural network in this study. The use of a self-organizing map to classify samples resulted in the following five categories: Low quantities of Sodium Na (mg/l), Potassium k(mg/l), Magnesium Mg(mg/l), Calcium Ca(mg/l), Sulfates SO4(mg/l), and Total Dissolved Solids TDS (mg/l) distinguish Classes 2 and 3. Bicarbonate HCO3 (mg/l), Total Dissolved Solids TDS (mg/l), Total Alkalinity CaCO3(mg/l), Mg(mg/l), Calcium Ca (mg/l), and electrical conductivity Cond (ms/cm) are slightly greater in Classes 1 and 4. Except for Dissolved Oxygen D.O. (mg/l) and Nitrate NO3(mg/l), Class 5 has exceptionally high values for all metrics. The results suggest that Kohonen's self-organizing topological maps (SOM) classification is an outstanding and fundamental tool for understanding and displaying the spatial distribution of water physicochemical quality.

The principal purpose of this study is to build stochastic neuronal models, for the prediction of heavy metal, contents in the surface waters of the Oued Inaouen catchment area of the TAZA region, according to their Physico-chemical parameters; we have carried out a comparative study: the multiple linear regression (MLR) method and the artificial neural network (ANN) approach. The following statistical indicators were used to evaluate the performance of the stochastic models developed by neural network and MLR: The sum of the quadratic errors (SSE) and the determination coefficient (R²), also through the study of fit graphs. The results show that the predictive modelling using artificial neural networks is very effective. This performance shows a non-linear relation between the studied Physico-chemical characteristics and the heavy metal contents in the surface waters of the Oued Inaouen catchment area.

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen.

This paper describes how the multilayer perceptron neural network (MLPNN) trained by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-newton back-propagation approach was used to estimate heavy metal concentrations: Aluminum (Al), Lead (Pb), Copper (Cu), and Iron (Fe), in the province of Taza using sixteen physicochemical factors measured from 100 samples collected from surface water sources by our team, according to the protocol of the national water office (ONE). We chose a network with only one hidden layer to identify the network architecture to employ. The number of neurons in the hidden layer was varied, as were the types of transfer and activation functions, and the BFGS learning method was used. The following statistical metrics were used to evaluate the performance of the neural network’s stochastic models: Examining the adjustment graphs and residue, as well as the Error Sum of Squares (SSE); the mean bias error (MBE) and determination coefficient (R²). The results reveal that the predictive models created using the artificial neural network method (ANN) are quite efficient, thanks to the BFGS algorithm’s efficiency and speed of convergence. An architectural network [16-8-1] (16: number of variables in input layer, 8: number of hidden layer, 1: number of variables in output layer) produced the best results,{R²: Al(0.954), Pb(0.942), Cu(0.921), Fe(0.968)}, {SSE: Al(0.396), Pb(0.0059), Cu(0.252), Fe(4.29)} and {MBE: Al(–0.033), Pb(0.008), Cu(–0.004), Fe(0.091)}, which is developed so that each model is responsible for estimating the concentration of a single heavy metal. This result demonstrates that there is a non-linear relationship between the physical-chemical properties evaluated and the heavy metal content of surface water in the Taza province.

This paper adopts two modeling tools, namely, multiple linear regression (MLR) and artificial neural networks (ANNs), to predict the concentrations of heavy metals (zinc, boron, and manganese) in surface waters of the Oued Inaouen watershed flowing towards Inaouen, using a set of physical-chemical parameters. XLStat was employed to perform multiple linear and nonlinear regressions, and Statista 10 was chosen to construct neural networks for modeling and prediction. The effectiveness of the ANN- and MLR-based stochastic models was assessed by the determination coefficient (R²), the sum squared error (SSE) and a review of fit graphs. The results demonstrate the value of ANNs for prediction modeling. Drawing on supervised learning and back propagation, the ANN-based prediction models adopt an architecture of [18-15-1] for zinc, [18-11-1] for manganese, and [18-8-1] for boron, and perform effectively with a single cached layer. It was found that the MLR-based prediction models are substantially less accurate than those based on the ANNs. In addition, the physical-chemical parameters being investigated are nonlinearly correlated with the levels of heavy metals in the surface waters of the Oued Inaouen watershed flowing towards Inaouen.