Rapid and Accurate Prediction of Fluvial Floods in Temporal and Spatial Dimensions by a Deep Residual Network Model with Squeeze-and-Excitation Attention

Key numbers

• 400 times over traditional PBH models

Frequent flooding induced by climate change has been posing growing threats to human 2 lives and property worldwide. Most of 2D and 3D physics-based hydrodynamic (PBH) models are generally too time-consuming to support decision-makers to react in time during an urgent flood event. Therefore, highly efficient and accurate flood inundation prediction models are urgently needed to improve emergency response capabilities. In this paper, a novel model based on a 1D deep residual neural network with Squeeze-and-Excitation (ResNet-SE) attention is proposed for fast forecasting dynamic evolution of 2D fluvial flood inundation. Different from widely-used 2D image-oriented DL models, our model directly consumes original 1D nodal hydraulic output vectors from TELEMAC-2D, avoiding interpolation errors introduced by mesh rasterization. The results of the well-trained ResNet-SE model compare with those from the TELEMAC-2D and a convolutional neural network (CNN) model. As indicated by several quantitative assessment matrices, the present model outperforms CNN model and conforms closely to the TELEMAC-2D in high precision. Furthermore, we demonstrate that ResNet34-SE (34 layers) stands out distinctly among ResNet-SEs of 18 and 50 layers in flood inundation prediction. The average MAE and RMSE metrics of ResNet34-SE under three unseen flood events reach 0.04623 m and 0.08052 m in water depth prediction. As for spatial flood extent assessment, this model also delivers impressive performance, with the average Precision, Recall, F1, and Accuracy scores reaching 96.92%, 99.69%, 98.28%, and 98.55%, respectively. Furthermore, current deep-learning (DL) model achieve a speedup of 300 to 400 times over traditional PBH models, enabling the generation of 24-h flood evolution maps within seconds. The proposed ResNet-SE model demonstrates good simulating accuracy of fluvial flood dynamics for proactive emergency warning systems and flood risk mitigation planning within the Lushui River basin.

Chao Tong, Yuan Yuan, Liang Sun, Aiming Liu, Yinjun Zhou

Leverage3 / 5

A method or resource many groups across the field will adopt within a year.

16% of this desk’s rubric

Magnitude3 / 5

Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.

20% of this desk’s rubric

Evidence3 / 5

Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.

20% of this desk’s rubric

Novelty2 / 5

A new combination of known ideas.

10% of this desk’s rubric

Trajectory2 / 5

Some room to improve with obvious engineering.

14% of this desk’s rubric

Stakes3 / 5

Meaningful benefit to many people within a few years.

20% of this desk’s rubric

• Citations0 (reference 15, via openalex, Oct 29, 2026, 07:29 UTC)

• Reviewed by heuristic-v10 on Oct 29, 2026, 07:29 UTC. Paper type: method.
• Categories: Flood Risk Assessment and Management, Hydrological Forecasting Using AI, Model Reduction and Neural Networks, Global and Planetary Change, Environmental Science
• TOP, No.3 in the Climate & Energy edition of October 29, 2026.