# ML framework for global river flood predictions based on the Caravan dataset

arXiv: https://arxiv.org/abs/2212.00719 · HTML: https://mlsanity.com/paper/2212.00719/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets](https://mlsanity.com/paper/1907.08456/) (2019)
- [Combining Static and Dynamic Features for Multivariate Sequence Classification](https://mlsanity.com/paper/1712.08160/) (2017)
- [ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach](https://mlsanity.com/paper/2012.00671/) (2020)

## Abstract (arXiv)

Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river flood prediction models already exist and perform relatively well in most high-income countries. But, due to the limited availability of data, these models are lacking in low-income countries. Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset. Our framework aims to serve as a benchmark for future global river flood prediction research. To support generalizability claims we include custom data evaluation splits. Further, we propose and evaluate a novel two-path LSTM architecture (2P-LSTM) against three baseline models. Finally, we evaluate the generated models on different locations in Africa and Asia that were not part of the Caravan dataset.