Abstract
Forest fire prediction is very important for protecting forests from fire. This is a severe environmental issue that is destroying the ecosystem’s ecology. It raises the crisis of other logical disasters and depletes resources like water, contributing to global warming and pollution. The detection of fire is a critical component of incident control. Forest fire prediction is supposed to lessen the future impact of forest fire. There are many algorithms to help detect forest fires, each of which has a different approach to fire detection. The current processes anticipate the fire-affected zone based on satellite imagery, which isn’t entirely accurate. The proposed system uses weather conditions such as rain, humidity, wind, and temperature to anticipate the occurrences of forest fires. We employed multiple sub-samples of the dataset on which the RandomizedSearchCV method fitted many decision trees and also used averaging to improve the accuracy. We will explore many models for predicting forest fires in this study.
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Bhavatarini, N., Santhosh, S., Balaji, N., Kumari, D. (2024). An Approach to Conserve Wildlife Habitat by Predicting Forest Fire Using Machine Learning Technique. In: Guru, D.S., Kumar, N.V., Javed, M. (eds) Data Analytics and Learning. ICDAL 2022. Lecture Notes in Networks and Systems, vol 779. Springer, Singapore. https://doi.org/10.1007/978-981-99-6346-1_5
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