INA-FOREWS: An IoT-Based Multi-Site Indonesia Forest Fire Early Warning System with Real-Time Risk Assessment and Alert Capabilities

Abstract

Despite the limitations of existing forest fire detection solutions, this study proposes the development of an IoT-based INA-FOREWS system to enhance real-time monitoring and risk assessment capabilities in Indonesia's tropical forests. The system aims to mitigate the impacts of forest fires and support progress towards the Sustainable Development Goals (SDGs) by integrating local sensor data with global spatial datasets. The system utilizes a rule-based approach to analyze environmental variables such as temperature, humidity, precipitation, and vegetation index, generating interactive fire risk maps to inform more effective response strategies. The hardware components, including sensors for temperature, humidity, gas, flame, and soil moisture, are deployed across multiple strategic locations and connected to a central ESP32 microcontroller for data transmission to a cloud-based platform. The web-based interface provides visualizations of sensor data, fire risk assessments, and emergency notification features to support decision-making by policymakers, local communities, and emergency responders. The strategic placement of sensors, guided by the rule-based risk assessment model, helps to optimize coverage and enhance the system's capacity for early fire detection. The proposed INA-FOREWS system serves as a model for integrating technological innovation with sustainability-focused objectives, contributing to improved forest fire management practices and supporting ecosystem preservation and community well-being in Indonesia's tropical regions.