Vol. 4 No. 3 (2025): July
RESEARCH ARTICLES

A Smart Prediction System for Forest Fires Using Cellular Automata and Machine Learning

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  • S Mounika,
  • V Rizwana,
  • S Hussain Basha,
  • S Mohammed Ismail,
  • T Anusha
S Mounika
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
V Rizwana
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Hussain Basha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Mohammed Ismail
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
T Anusha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15210665

Published 2025-04-14

Keywords

  • Forest fire prediction,
  • forest fire behavior,
  • cellular automata,
  • Wang Zhengfei model,
  • machine learning

How to Cite

S Mounika, V Rizwana, S Hussain Basha, S Mohammed Ismail, & T Anusha. (2025). A Smart Prediction System for Forest Fires Using Cellular Automata and Machine Learning. International Journal of Computational Learning & Intelligence, 4(3), 522–528. https://doi.org/10.5281/zenodo.15210665

Copyright (c) 2025 S Mounika, V Rizwana, S Hussain Basha, S Mohammed Ismail, T Anusha

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Forest fires represent a widespread and devastating natural disaster affecting  millions of hectares of forest land each year and posing significant risks to both human lives and property. Timely and accurate predictions of forest fire behavior are crucial for developing effective risk management strategies and improving firefighting responses. This study introduces an innovative Forest Fire Spread Behavior Prediction (FFSBP) model, which integrates two key components: the Forest Fire Spread Process Prediction (FFSPP) model and the Forest Fire Spread Results Prediction (FFSRP) model. The FFSPP model leverages a combination of advanced methodologies to forecast the dynamics of fire spread, while the FFSRP model aims to predict the final outcomes of fire events.Moreover, the FFSRP model demonstrates strong predictive capabilities, particularly for smaller and medium-scale fire scenarios. These results highlight the potential of the FFSBP model as a powerful tool for improving the accuracy of forest fire predictions and supporting more effective risk management and firefighting operations.

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