Inundation Modeling in Data Scarce Regions
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Oct 2019.
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Citing papers per year: 2021: 3; 2022: 1.
4 papers.
- 2021Machine learning accelerated computational fluid dynamicscited by 461
- 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 25
- 2021Flood forecasting with machine learning models in an operational frameworkcited by 14
- 2021Physics-Aware Downsampling with Deep Learning for Scalable Flood Modelingcited by 4
- 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents11 Jan 2022
- 2021Flood forecasting with machine learning models in an operational framework4 Nov 2021
- 2021Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling14 Jun 2021
- 2021Machine learning accelerated computational fluid dynamics28 Jan 2021