Enhancing Forest Fire Prediction through Machine Learning and IoT Integration

Abstract

The increasing threat of forest fires, exacerbated by climate change and environmental factors, necessitates efficient prediction and management systems. This research aims to develop a predictive model for forest fire occurrence using machine learning techniques and weather data. Data was collected from historical records of fire occurrences and environmental conditions such as temperature, humidity, wind speed, and rainfall. After undergoing preprocessing steps including data cleaning, handling missing values, and feature scaling, various machine learning models were evaluated, including XGBoost, Random Forest, K-Nearest Neighbors, Decision Trees, and Logistic Regression. Among these, XGBoost and Random Forest models exhibited the highest predictive accuracy, achieving an accuracy score of 97.52%. The models provided valuable insights into the environmental factors contributing to fire risks, enabling more informed decision-making for fire prevention. The integration of advanced algorithms in this system demonstrates the potential for proactive forest fire management, reducing damage, enhancing resource allocation, and improving overall fire risk mitigation strategies. The findings underscore the effectiveness of machine learning in environmental risk management, paving the way for more sustainable and efficient forest fire prediction systems.