Multi Sensor Network System for Early Detection and Prediction of Forest Fires in Southeast Asia

The increasing frequency and severity of forest and land fires have become a significant environmental concern, necessitating the development of effective early detection and prediction systems. This paper presents a novel approach to address the issue through the implementation of a multi-sensor network system for forest and land fires. The proposed system integrates an array of advanced multi-sensors strategically placed across the targeted regions to capture and analyze a wide range of fire-related data. The key objective of the system is to enable timely identification of potential fire hotspots by continuously monitoring various environmental parameters, including temperature, humidity, and infrared radiation. The collected data is then processed and analyzed using machine learning algorithms to identify fire patterns and predict the likelihood of fire outbreaks. The system utilizes a network of sensors, and the system offers real-time and comprehensive coverage, allowing for rapid response and timely deployment of fire suppression resources. Furthermore, the results of extensive field tests and evaluations, demonstrate the system's accuracy and efficiency in early fire detection and prediction. The proposed system offers a case in Indonesia which is Riau Province with high-risk cases almost every year. Plotting results data achieved and forecasting of the incident for the future in the year 2023 with a successful percentage up to 93.6%. Ultimately, the integration of the multi-sensor network system into existing fire management frameworks promises to enhance emergency response capabilities and foster proactive measures to preserve our valuable forests and lands.