Statistical Modelling by Topological Maps of Kohonen for Classification of the Physicochemical Quality of Surface Waters of the Inaouen Watershed Under Matlab

Abstract

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.