A System for Efficient Detection of Forest Fires through Low Power Environmental Data Monitoring and AI

This study introduces a system that merges AI with low-power IoT (Internet Of Things) technology to enhance environmental monitoring, with a specific focus on accurately predicting forest fires through time series analysis. Utilizing affordable sensors and wireless communication technologies like LoRa (Long Range), environmental data have been gathered. One of the key features of this approach is the comparison of the real-time local environmental data with meteorological service environmental data to ensure accuracy. This comparison informs a feedback loop that improves the model's predictive accuracy. The research also delves into detailed time series analysis, incorporating the Autoregressive Integrated Moving Average (ARIMA) model to identify the best windows of opportunity for communication and to provide future forecasting. Finally, a decision tree model serves as the last step, providing a comprehensive assessment of fire risk due to its straightforward application and clarity. Validation of the fire detection component remains a critical future task to confirm its effectiveness and reliability.