Real-Time Flood Prediction Using Remote Sensing and Edge Computing

Abstract

The objective of this research is to devise a scalable approach for real-time flood prediction utilizing remote sensing data and edge computing technologies. This system possesses high precision and low latency, optimizing it for disaster management. This approach includes the fetching of the required remote sensing data through satellites and drones, subsequent processing of the data and running the ML model on edge devices. The results achieved with the proposed framework show reduction in latency and improved data accuracy, allowing quick warning signals to be sent. The work underscores the need of integrating these technologies to improve resiliency and responsiveness to humanitarian aid in disaster regions.