# Flood Prediction Using Machine Learning Models: Literature Review

arXiv: https://arxiv.org/abs/1908.02781 · HTML: https://mlsanity.com/paper/1908.02781/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 30 of 36)

- [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://mlsanity.com/paper/2307.01909/) (2023)
- [User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems](https://mlsanity.com/paper/2306.07526/) (2023)
- [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)
- [Improving Flow Matching by Aligning Flow Divergence](https://mlsanity.com/paper/2602.00869/) (2026)
- [Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning](https://mlsanity.com/paper/2107.08369/) (2021)
- [A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing](https://mlsanity.com/paper/2312.11754/) (2023)
- [FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation](https://mlsanity.com/paper/2402.13371/) (2024)
- [Global Flood Prediction: a Multimodal Machine Learning Approach](https://mlsanity.com/paper/2301.12548/) (2023)
- [The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation](https://mlsanity.com/paper/2310.19166/) (2023)
- [Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review](https://mlsanity.com/paper/2207.07580/) (2022)
- [Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates](https://mlsanity.com/paper/2310.07631/) (2023)
- [Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations](https://mlsanity.com/paper/2406.15451/) (2024)
- [RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation](https://mlsanity.com/paper/2602.00849/) (2026)
- [Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection](https://mlsanity.com/paper/2503.14754/) (2025)
- [RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting](https://mlsanity.com/paper/2505.22535/) (2025)
- [Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion](https://mlsanity.com/paper/2304.09994/) (2023)
- [Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquake](https://mlsanity.com/paper/2311.08999/) (2023)
- [Towards Generalized Hydrological Forecasting using Transformer Models for 120-Hour Streamflow Prediction](https://mlsanity.com/paper/2406.07484/) (2024)
- [Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports](https://mlsanity.com/paper/2506.08740/) (2025)
- [Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning](https://mlsanity.com/paper/2510.26017/) (2025)
- [Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mapping](https://mlsanity.com/paper/2511.14033/) (2025)
- [ZAYAN: Disentangled Contrastive Transformer for Tabular Remote Sensing Data](https://mlsanity.com/paper/2604.27606/) (2026)
- [Second Order Drifting Models](https://mlsanity.com/paper/2608.07924/) (2026)
- [An evaluation of deep learning models for predicting water depth evolution in urban floods](https://mlsanity.com/paper/2302.10062/) (2023)
- [AI for Anticipatory Action: Moving Beyond Climate Forecasting](https://mlsanity.com/paper/2307.15727/) (2023)
- [MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features](https://mlsanity.com/paper/2308.06228/) (2023)
- [Data Science for Geographic Information Systems](https://mlsanity.com/paper/2404.03754/) (2024)
- [Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee](https://mlsanity.com/paper/2405.10375/) (2024)
- [Using Neural Networks for Data Cleaning in Weather Datasets](https://mlsanity.com/paper/2406.15027/) (2024)

## Abstract (arXiv)

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.