IMPROVE Floodeye: Integrated Mobile System for Predictive Routing and Optimized Vehicle Navigation Using Ensemble Algorithm

The increasing frequency of urban flooding necessitates effective solutions for real-time navigation and predictive routing. This study presents IMPROVE Floodeye, an integrated mobile system designed to optimize vehicle navigation using internet of things IOT and ensemble algorithm. The system collects and analyzes real-time flood data from various sources, including weather reports, sensors, and user-generated inputs. By leveraging an ensemble algorithm that combines machine learning models, it predicts flood-prone areas and recommends alternative routes to ensure safe and efficient travel. The mobile application provides users with dynamic updates, visual flood maps, and adaptive route suggestions. Evaluation results demonstrate the system's accuracy in flood prediction and routing optimization compared to conventional navigation systems. The implementation of IMPROVE Floodeye offers a scalable and intelligent solution for urban flood management, enhancing commuter safety and reducing travel time. Based on the findings on the Floodeye system, the following recommendations are proposed to enhance its effectiveness, sustainability, and scalability. These include the integration of various types of sensors to more accurately measure flood levels and rainfall intensity, and the expansion of the routing scheme to cover a wider geographic area for improved data coverage. Additionally, incorporating community-based reporting can boost situational awareness and the reliability of flood monitoring. Collaborating with local government units (LGUs) is essential to support system deployment, integrate data with disaster response protocols, and foster public trust and adoption. Lastly, conducting long-term system evaluations is crucial for guiding future improvements and ensuring the continued sustainability of the project.