# Flood forecasting with machine learning models in an operational framework

arXiv: https://arxiv.org/abs/2111.02780 · HTML: https://mlsanity.com/paper/2111.02780/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 70 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets](https://mlsanity.com/paper/1907.08456/) (2019)
- [Flood Prediction Using Machine Learning Models: Literature Review](https://mlsanity.com/paper/1908.02781/) (2019)
- [Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales](https://mlsanity.com/paper/1912.08949/) (2019)
- [HydroNets: Leveraging River Structure for Hydrologic Modeling](https://mlsanity.com/paper/2007.00595/) (2020)
- [Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling](https://mlsanity.com/paper/2012.14295/) (2020)
- [Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network](https://mlsanity.com/paper/2010.07921/) (2020)
- [Inundation Modeling in Data Scarce Regions](https://mlsanity.com/paper/1910.05006/) (2019)
- [ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach](https://mlsanity.com/paper/2012.00671/) (2020)

## Built on by (most cited first, 15 of 15)

- [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [The Merit of River Network Topology for Neural Flood Forecasting](https://mlsanity.com/paper/2405.19836/) (2024)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [Cross Modal Distillation for Flood Extent Mapping](https://mlsanity.com/paper/2302.08180/) (2023)
- [RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting](https://mlsanity.com/paper/2505.22535/) (2025)
- [Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting](https://mlsanity.com/paper/2409.18591/) (2024)
- [Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning](https://mlsanity.com/paper/2510.26017/) (2025)
- [An Early Warning Model for Forced Displacement](https://mlsanity.com/paper/2505.06249/) (2025)
- [Google, AI Literacy, and the Learning Sciences: Multiple Modes of Research, Industry, and Practice Partnerships](https://mlsanity.com/paper/2604.07601/) (2026)
- [Context-Aware Concept Distillation for Trustworthy Flood Prediction](https://mlsanity.com/paper/2607.23237/) (2026)
- [Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics](https://mlsanity.com/paper/2609.06109/) (2026)
- [Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale](https://mlsanity.com/paper/2609.22702/) (2026)
- [C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories](https://mlsanity.com/paper/2609.39005/) (2026)
- [Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation](https://mlsanity.com/paper/2610.15750/) (2026)

## Abstract (arXiv)

The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the Long Short-Term Memory (LSTM) networks and the Linear models. Flood inundation is computed with the Thresholding and the Manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The Manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the Linear model, while the Thresholding and Manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287,000 km2, home to more than 350M people. More than 100M flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations, as well as improving modeling capabilities and accuracy.