Naive Bayes is a simple, probabilistic machine learning algorithm used for classification tasks. It assumes that the features are conditionally independent given the class, making it efficient and effective for text classification and spam filtering applications.
This comprehensive review explores the integration of machine learning (ML) techniques in forest fire science. The study highlights the significance of early fire prediction and detection for effective fire management. It discusses various ML methods applied in forest fire detection, prediction, fire mapping, and data evaluation. The review identifies challenges and research priorities while emphasizing the potential benefits of ML in improving forest fire resilience and enabling more efficient data analysis and modeling.
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