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Forest fires pose a significant threat to both the environment and human life. This study presents a machine learning-based forest fire prediction model using various regression algorithms to estimate the burned area and severity of fire occurrences. Utilizing attributes like temperature, humidity, wind speed, and rainfall, the model aids in proactive management by offering detailed predictions beyond simple fire occurrences. The system leverages Support Vector Regression (SVR), Random Forest, Neural Networks, and Linear Regression, with a focus on providing actionable insights for resource allocation and firefighting strategy. Evaluation metrics include Mean Squared Error (MSE) and R-squared values, showing that [mention best model, e.g., Neural Network] offers the most accurate predictions for the Indian forest region dataset.

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Forest Fire Prediction Using Machine Learning Algorithms

  • 会议论文

  • 发表日期：

    2026年

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  • R. Vijaya Prakash,
  • Garlapati Nikesh Reddy,
  • Chinthakuntla Sathvik Reddy,
  • Manchala Teja Swaroop,
  • Ambati Krishna Sai,
  • Joonoori Manoj Kumar,
  • Gundeti Siddharth

摘要

Forest fires pose a significant threat to both the environment and human life. This study presents a machine learning-based forest fire prediction model using various regression algorithms to estimate the burned area and severity of fire occurrences. Utilizing attributes like temperature, humidity, wind speed, and rainfall, the model aids in proactive management by offering detailed predictions beyond simple fire occurrences. The system leverages Support Vector Regression (SVR), Random Forest, Neural Networks, and Linear Regression, with a focus on providing actionable insights for resource allocation and firefighting strategy. Evaluation metrics include Mean Squared Error (MSE) and R-squared values, showing that [mention best model, e.g., Neural Network] offers the most accurate predictions for the Indian forest region dataset.