mlsanityData to 7 Oct 2026

Flood Prediction Using Machine Learning Models: Literature Review

arXiv 1908.02781 ↗First posted 7 Aug 2019cs.LG stat.MLcited by 36

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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.

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

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 11; 2024: 7; 2025: 6; 2026: 9.

36 papers.

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  1. 2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modelingcited by 46
  2. 2023User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systemscited by 30
  3. 2021Flood forecasting with machine learning models in an operational frameworkcited by 14
  4. 2024The Merit of River Network Topology for Neural Flood Forecastingcited by 12
  5. 2026Improving Flow Matching by Aligning Flow Divergencecited by 9
  6. 2021Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learningcited by 8
  7. 2023A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcingcited by 6
  8. 2023Global Flood Prediction: a Multimodal Machine Learning Approachcited by 4
  9. 2024FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigationcited by 4
  10. 2022Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Reviewcited by 3
  11. 2023The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigationcited by 3
  12. 2024Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptationscited by 3
24 more
  1. 2026RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generationcited by 3
  2. 2023Graph Transformer Network for Flood Forecasting with Heterogeneous Covariatescited by 2
  3. 2025Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detectioncited by 2
  4. 2025RiverMamba: A State Space Model for Global River Discharge and Flood Forecastingcited by 2
  5. 2023Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusioncited by 1
  6. 2023Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquakecited by 1
  7. 2024Towards Generalized Hydrological Forecasting using Transformer Models for 120-Hour Streamflow Predictioncited by 1
  8. 2025Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reportscited by 1
  9. 2025Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learningcited by 1
  10. 2025Flood-LDM: Generalizable Latent Diffusion Models for rapid and accurate zero-shot High-Resolution Flood Mappingcited by 1
  11. 2026Second Order Drifting Modelscited by 1
  12. 2023An evaluation of deep learning models for predicting water depth evolution in urban floodscited by 0
  13. 2023AI for Anticipatory Action: Moving Beyond Climate Forecastingcited by 0
  14. 2023MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Featurescited by 0
  15. 2024Data Science for Geographic Information Systemscited by 0
  16. 2024Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennesseecited by 0
  17. 2024Using Neural Networks for Data Cleaning in Weather Datasetscited by 0
  18. 2025Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Changecited by 0
  19. 2026Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantificationcited by 0
  20. 2026ZAYAN: Disentangled Contrastive Transformer for Tabular Remote Sensing Datacited by 0
  21. 2026Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation modelscited by 0
  22. 2026Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibilitycited by 0
  23. 2026Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systemscited by 0
  24. 2026Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analyticscited by 0