# Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2004.08340 · HTML: https://mlsanity.com/paper/2004.08340/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 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)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [Rapid Flood Inundation Forecast Using Fourier Neural Operator](https://mlsanity.com/paper/2307.16090/) (2023)
- [Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations](https://mlsanity.com/paper/2406.15451/) (2024)
- [RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting](https://mlsanity.com/paper/2505.22535/) (2025)
- [A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping](https://mlsanity.com/paper/2506.13201/) (2025)
- [Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion](https://mlsanity.com/paper/2304.09994/) (2023)
- [A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia](https://mlsanity.com/paper/2307.14185/) (2023)
- [Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning](https://mlsanity.com/paper/2510.26017/) (2025)
- [Towards Daily High-resolution Inundation Observations using Deep Learning and EO](https://mlsanity.com/paper/2208.09135/) (2022)
- [An evaluation of deep learning models for predicting water depth evolution in urban floods](https://mlsanity.com/paper/2302.10062/) (2023)
- [Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?](https://mlsanity.com/paper/2403.08589/) (2024)
- [A Deep U-Net Framework for Flood Hazard Mapping Using Hydraulic Simulations of the Wupper Catchment](https://mlsanity.com/paper/2604.21028/) (2026)
- [Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility](https://mlsanity.com/paper/2607.25148/) (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)

## Abstract (arXiv)

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning.