Prediction of Forest Fires Using Logistic Regression

EasyChair Preprint 9901

Abstract

 An important aspect of managing forest fires is forest fire prediction. It is crucial to efforts for resource allocation, mitigation, and recovery. In this essay, machine learning-based strategies for predicting forest fires are described and analysed. The research innovative logistic regression-based forest fire risk prediction system is presented. The system uses historical weather data to forecast the likelihood of a fire on a given day. Through the use of historical data, a correlation between forest fire producing elements and forest fire occurrence is being established in this study project. We can find out when there is a high risk of forest fires by using the system, and forest guards can pay particular attention to preventing forest fires at those times.

Keyphrases: Weather, forest fire, historical data, logistic regression, machine learning, prediction

@booklet{EasyChair:9901,
  author    = {Adhikari Durga Venkata Madhav and Addanki Gargeya and Amanchi Sravan and Chimata Suman},
  title     = {Prediction of Forest Fires Using Logistic Regression},
  howpublished = {EasyChair Preprint 9901},
  year      = {EasyChair, 2023}}