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In this study, Kohonen’s self-organizing maps (SOM) were applied to assess environmental challenges by mapping and categorizing physicochemical factors in the Inaouen watershed. Using a classification method based on the SOM artificial neural network, five distinct clusters were identified in the water quality of the region. Classes 2 and 3 showed low of sodium, potassium, magnesium, calcium, sulfates, and total dissolved solids. With respect to classes 1 and 4, they showed higher values of bicarbonates (HCO3), total dissolved solids (TDS), total alkalinity (CaCO3), magnesium (Mg), calcium (Ca), and electrical conductivity. Most of the parameters were found to be extremely high for Class 5 except the D.O. and NO3, which indicates localized water quality issues in certain areas. The research highlights successfully using Kohonen’s self-organizing map classification technique in evaluating the spatial distribution of water quality. The study of SOM offers great insight into the environment of the members of the Inaouen basin, thus improving the understanding of the very complex ecosystem. Thus, it helps the researchers to reach a better decision-making capacity to implement proper management in water resources.

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A Mathematical Algorithmic Analysis of Water Quality Variability Using Kohonen’s Self-organizing Maps

  • 章节

  • 发表日期：

    2025年

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  • R. El Chaal,
  • M. O. Aboutafail

摘要

In this study, Kohonen’s self-organizing maps (SOM) were applied to assess environmental challenges by mapping and categorizing physicochemical factors in the Inaouen watershed. Using a classification method based on the SOM artificial neural network, five distinct clusters were identified in the water quality of the region. Classes 2 and 3 showed low of sodium, potassium, magnesium, calcium, sulfates, and total dissolved solids. With respect to classes 1 and 4, they showed higher values of bicarbonates (HCO3), total dissolved solids (TDS), total alkalinity (CaCO3), magnesium (Mg), calcium (Ca), and electrical conductivity. Most of the parameters were found to be extremely high for Class 5 except the D.O. and NO3, which indicates localized water quality issues in certain areas. The research highlights successfully using Kohonen’s self-organizing map classification technique in evaluating the spatial distribution of water quality. The study of SOM offers great insight into the environment of the members of the Inaouen basin, thus improving the understanding of the very complex ecosystem. Thus, it helps the researchers to reach a better decision-making capacity to implement proper management in water resources.