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This paper presents a comprehensive study contributing to soil pollution research by applying machine learning techniques. The research explores predicting soil heavy metals and pollution indices, identifying, and classifying potentially polluting enterprises, and reviewing machine learning models for soil pollution prediction. The study employs various machine learning algorithms, including linear regression, decision tree regressor, and k nearest neighbor, to predict soil pollution at depths not physically investigated. This approach proves instrumental in reducing the time, staff, and resources required for pollution measurement in the Absheron Peninsula.

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Prediction of Soil Pollution Using Machine Learning Methods in Absheron Peninsula

  • 会议论文

  • 发表日期：

    2025年

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  • Maya Abdullayeva,
  • Tunzala Babayeva,
  • Shebnem Dadashova

摘要

This paper presents a comprehensive study contributing to soil pollution research by applying machine learning techniques. The research explores predicting soil heavy metals and pollution indices, identifying, and classifying potentially polluting enterprises, and reviewing machine learning models for soil pollution prediction. The study employs various machine learning algorithms, including linear regression, decision tree regressor, and k nearest neighbor, to predict soil pollution at depths not physically investigated. This approach proves instrumental in reducing the time, staff, and resources required for pollution measurement in the Absheron Peninsula.