BAHAGAP: Design and Development of an Image Processing-Based Detection and Monitoring System for Early Flood Warnings in High-Risk Zones

Flooding is one of the major natural disasters that poses serious threats to human life, property, and ecological systems, especially in areas prone to inundation like Calamba, Laguna, Philippines. The rising occurrence of flooding due to climate change and urbanization emphasizes the need for effective early warning systems to mitigate its impacts. The BAHAGAP flood detection and monitoring system developed here runs on solar energy. It uses the techniques of image processing to improve early warning functionalities and strengthen community resilience. BAHAGAP uses advanced image processing to monitor flood levels in real-time and integrates hardware components such as rain gauges, Raspberry Pi 4B microcontrollers, and a solar power system to ensure uninterrupted operation during power outages common in extreme weather. The system provides a sustainable and efficient solution specific to high-risk zones and addresses weaknesses in traditional flood management strategies. Its energy-efficient design, scalability, and ease of deployment make it adaptable to other flood-prone regions. The image processing helps detect the actual flood level with precision and also sends timely warnings to local governments and residents so that they may make informed decisions. In addition, a flood detection model is also trained to obtain high system performance. BAHAGAP integrates hardware reliability with software capabilities, thus giving a holistic approach to flood surveillance. The new system has been a step forward in flood identification and early alert systems to meet the ever-growing need for sustainable solutions to reducing the impact of flooding. The study calls attention to the fundamental role of technology in enhancing resilience to disasters and puts BAHAGAP as an example for further flood management schemes in vulnerable regions.