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The Yamuna River holds significant importance for environmental scientists and indigenous communities due to its social, ethical, and geographical significance. The city of Delhi is marked by ongoing anthropogenic development and various festivals that have a significant detrimental impact on the quality of surface waters. This study specifically examined the period following festivals from September to November spanning from 2012 to 2021, including Ganesh Chaturthi, Durga Puja, and Chhath Puja, during which non-biodegradable idols are immersed in the river. Three distinct sampling locations were selected to analyze the variations in Electrical Conductivity (EC), pH, Dissolved Oxygen (DO), and Lead (Pb) levels in the waters of the Yamuna. The presence of lead, a key indicator of contamination from paints, was observed to range from 0 to 1.80 mg/L throughout the study period, significantly exceeding the acceptable value of 10 µg/L. The data has been analyzed within a GIS environment, using Kriging for spatial distribution mapping alongside the Classified Land Use Land Cover map employing the Support Vector Machine Algorithm. R programming has been utilized to conduct the Mann–Kendall test, the Sequential Mann–Kendall test, and Sen’s slope analysis. The findings indicate a significant increase in overall concentrations of the parameters, largely attributed to waste fluids flowing through numerous drains. Dissolved oxygen (DO) values suggest limited support for aquatic life. However, the electrical conductivity (EC) concentration decreased, while the pH and DO marginally increased, implying an improvement in Yamuna water quality during the lockdown period. This study aims to lay the groundwork for more effective water management strategies in the region. We propose stringent regulations for monitoring the release of chemically reactive compounds into surface waters resulting from industrial and other human activities to facilitate the restoration of the river to its natural environmental state. Additionally, we recommend the use of natural colors derived from flowers, leaves, seeds, bark, wood, and roots of plants to prevent contamination. As current Machine Learning Technologies advance, water quality studies are expected to improve.

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Application of a Machine Learning Algorithm and Geospatial Techniques to Assess the Post-festival Impact on Yamuna River Water Quality in Delhi

  • 章节

  • 发表日期：

    2025年

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  • Rahat Zehra,
  • Santosh Pal Singh,
  • Jyoti Verma

摘要

The Yamuna River holds significant importance for environmental scientists and indigenous communities due to its social, ethical, and geographical significance. The city of Delhi is marked by ongoing anthropogenic development and various festivals that have a significant detrimental impact on the quality of surface waters. This study specifically examined the period following festivals from September to November spanning from 2012 to 2021, including Ganesh Chaturthi, Durga Puja, and Chhath Puja, during which non-biodegradable idols are immersed in the river. Three distinct sampling locations were selected to analyze the variations in Electrical Conductivity (EC), pH, Dissolved Oxygen (DO), and Lead (Pb) levels in the waters of the Yamuna. The presence of lead, a key indicator of contamination from paints, was observed to range from 0 to 1.80 mg/L throughout the study period, significantly exceeding the acceptable value of 10 µg/L. The data has been analyzed within a GIS environment, using Kriging for spatial distribution mapping alongside the Classified Land Use Land Cover map employing the Support Vector Machine Algorithm. R programming has been utilized to conduct the Mann–Kendall test, the Sequential Mann–Kendall test, and Sen’s slope analysis. The findings indicate a significant increase in overall concentrations of the parameters, largely attributed to waste fluids flowing through numerous drains. Dissolved oxygen (DO) values suggest limited support for aquatic life. However, the electrical conductivity (EC) concentration decreased, while the pH and DO marginally increased, implying an improvement in Yamuna water quality during the lockdown period. This study aims to lay the groundwork for more effective water management strategies in the region. We propose stringent regulations for monitoring the release of chemically reactive compounds into surface waters resulting from industrial and other human activities to facilitate the restoration of the river to its natural environmental state. Additionally, we recommend the use of natural colors derived from flowers, leaves, seeds, bark, wood, and roots of plants to prevent contamination. As current Machine Learning Technologies advance, water quality studies are expected to improve.