English

Inundation Modeling in Data Scarce Regions

Machine Learning 2019-10-31 v2 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.1910.05006,
  title  = {Inundation Modeling in Data Scarce Regions},
  author = {Zvika Ben-Haim and Vladimir Anisimov and Aaron Yonas and Varun Gulshan and Yusef Shafi and Stephan Hoyer and Sella Nevo},
  journal= {arXiv preprint arXiv:1910.05006},
  year   = {2019}
}

Comments

To appear in the Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (AI+HADR) @ NeurIPS 2019

R2 v1 2026-06-23T11:40:38.460Z