Version 1
: Received: 8 December 2023 / Approved: 8 December 2023 / Online: 8 December 2023 (10:26:24 CET)
How to cite:
Khosravi, H.; Shafie, M. R.; Raihan, A. S.; Syed, M. A. B.; Ahmed, I. Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation. Preprints2023, 2023120577. https://doi.org/10.20944/preprints202312.0577.v1
Khosravi, H.; Shafie, M. R.; Raihan, A. S.; Syed, M. A. B.; Ahmed, I. Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation. Preprints 2023, 2023120577. https://doi.org/10.20944/preprints202312.0577.v1
Khosravi, H.; Shafie, M. R.; Raihan, A. S.; Syed, M. A. B.; Ahmed, I. Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation. Preprints2023, 2023120577. https://doi.org/10.20944/preprints202312.0577.v1
APA Style
Khosravi, H., Shafie, M. R., Raihan, A. S., Syed, M. A. B., & Ahmed, I. (2023). Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation. Preprints. https://doi.org/10.20944/preprints202312.0577.v1
Chicago/Turabian Style
Khosravi, H., Md Asif Bin Syed and Imtiaz Ahmed. 2023 "Optimizing Forest Fire Prediction: A Comparative Analysis of Machine Learning Models through Feature Selection and Time-Stage Evaluation" Preprints. https://doi.org/10.20944/preprints202312.0577.v1
Abstract
Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.
Keywords
Forest Fire Prediction; CRISP-DM Methodology; Feature Selection; Decision Tree
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
