# ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach

arXiv: https://arxiv.org/abs/2012.00671 · HTML: https://mlsanity.com/paper/2012.00671/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Flood forecasting with machine learning models in an operational framework](https://mlsanity.com/paper/2111.02780/) (2021)
- [The Merit of River Network Topology for Neural Flood Forecasting](https://mlsanity.com/paper/2405.19836/) (2024)
- [Transformer-based Flood Scene Segmentation for Developing Countries](https://mlsanity.com/paper/2210.04218/) (2022)
- [ML framework for global river flood predictions based on the Caravan dataset](https://mlsanity.com/paper/2212.00719/) (2022)

## Abstract (arXiv)

Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the world's vulnerable population does not have access to reliable and actionable warning systems, due to core challenges in scalability, computational costs, and data availability. In this paper we present two components of flood forecasting systems which were developed over the past year, providing access to these critical systems to 75 million people who didn't have this access before.