Literature Review: Development of a Machine Learning-Based Early Warning System for Land and Forest Fires with IoT and Automated Action Recommendations
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Abstract
Lightning strikes pose significant threats to human safety and infrastructure, particularly in tropical regions like Indonesia with high lightning activity. This study aims to develop a predictive model of lightning strike risk to humans based on spatial analysis and environmental factors, utilizing data on lightning distribution, land use, population density, and meteorological parameters. Using probabilistic decision trees and tropical lightning formulas, the model identifies key predictors, including rainfall, land use patterns, and humidity, which influence lightning density. The results reveal that densely populated areas with high lightning activity, such as parts of Java and Sumatra, are particularly vulnerable. Spatial risk maps generated from the model highlight high-risk zones, providing critical insights for disaster mitigation planning and infrastructure protection. Furthermore, the study emphasizes the significant correlation between lightning density, land use, and population exposure, offering a comprehensive framework for understanding lightning risks. This predictive model not only serves as a tool for early warning systems and sustainable spatial planning but also underscores the importance of integrating environmental and spatial data for effective lightning risk mitigation. Future research should incorporate temporal lightning variations and field validation to refine the model and enhance its applicability.
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