Deep Learning for Flood Forecasting and Monitoring in Urban Environments

Abstract

This paper describes the core computational mechanisms used by an urban flood forecasting and monitoring platform developed as part of a UK Newton Fund project in Malaysia. FLUD-FLood monitoring and forecasting platform for Urban Deployment - is a novel system aiming to deliver an effective and low cost urban flood forecasting solution, which is able to accurately forecast flood risk at street level, and deliver optimized recommendations to the relevant authorities as well as an early warning alerts to members of the public. This platform is based on a hybrid Deep Learning and Fuzzy Logic based architecture. As demonstrated by the experimental results and the analysis presented in this paper, this architecture enables the proposed system to account for factors that are not included in other modern flood forecasting systems, and simultaneously process high volumes of data originating from diverse data sources, in order to deliver accurate predictions concerning urban flood events.