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Prediction of Forest Fires Using Logistic Regression

EasyChair Preprint no. 9901

11 pagesDate: March 31, 2023


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

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  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 no. 9901},

  year = {EasyChair, 2023}}
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