Flood Early Warning Monitoring System Using KNN Methods In Bangkalan District

Bangkalan Regency faces serious challenges due to flood disasters that periodically threaten the safety and welfare of the community. The flood phenomenon in this area is caused by a complex combination of geographic and climate factors, including increased extreme rainfall and the dynamics of sea level rise. Annual floods not only cause infrastructure damage but also threaten the livelihoods and lives of residents living around the river flow. This study aims to develop an innovative flood early warning system using the K-Nearest Neighbors (KNN) method to predict potential disasters before floods occur. By using water flow data analysis and machine learning algorithms, this system is designed to provide accurate and timely early estimates. The main advantage of this study is its ability to proactively mitigate disaster risks using modern computer technology. The study produced a prototype of a flood detection and simulation system that can help local governments, related agencies, and the Bangkalan community in taking preventive and mitigation actions at an earlier stage. Therefore, this system is expected to make a significant contribution to reducing the impact of disasters and protecting the lives of Bangkalan Regency residents.