2019/10/11 by Zvika Ben‐Haim, Ben-Haim, Zvika, Anisimov, Vladimir +10 · 2 citations
Environmental Science · #FOS: Computer and information sciences #Flood Risk Assessment and Management #Hydrological Forecasting Using AI #Hydrology and Watershed Management Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1910.05006
openalex publication_date 2019/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providing spatially accurate forecasts is a challenge due to scarcity of data and lack of funding. This paper describes an operational system providing flood extent forecast maps covering several flood-prone regions in India, with the goal of being sufficiently scalable and cost-efficient to facilitate the establishment of effective flood forecasting systems globally.