Flood Water Depth Prediction with Convolutional Temporal Attention Networks
Abstract
:1. Introduction
- We propose a deep learning approach that predicts water depths at high spatial resolution for a two-hour lead time, given elevation data, water depth at starting time, and rainfall intensity values for the forecast duration.
- We introduce the new, large-scale pluvial flood dataset SwissFlood (which is available for download at https://doi.org/10.5281/zenodo.7797844) and make it publicly available. It contains pluvial floods for 100 catchments in Switzerland generated with a hydrodynamic flood model [17]. The rainfall events were extracted from rainfall observation data of the last forty years collected in Switzerland by MeteoSwiss [18].
- In our study, we aim to analyse the ability of our data-driven model to generalise the prediction of future water depth across catchments and rainfall events. All rainfall events used in this study are unique. We provide a quantitative as well as a qualitative analysis of our approach to evaluate its performance.
2. Related Work
3. Datasets
3.1. Catchments
3.2. Rainfall Data
3.3. Water Depth
4. Methodology
- A raster map of terrain elevations for catchment c.
- Water depth at time-step for catchment c.
- Rainfall intensity values for the two-hour time window between starting time t and target time . The intensity is discretised to steps of 10 min, i.e., the input consists of 12 rainfall values .
4.1. Deep Learning Model
4.2. Model Training
5. Results
Ablation Studies
6. Discussion and Conclusions
6.1. Limitations of the Proposed Approach
6.2. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Mean Absolute Errors (cm) | |||||||
---|---|---|---|---|---|---|---|
all | ≥5 | ≥10 | ≥20 | ≥50 | ≥100 | ||
Baseline-1 | unseen | 1.61 | 28.29 | 46.75 | 67.54 | 97.37 | 117.82 |
seen | 1.45 | 21.15 | 35.51 | 54.87 | 86.60 | 105.35 | |
Baseline-2 | unseen | 2.40 | 27.33 | 44.41 | 63.51 | 91.65 | 112.05 |
seen | 2.07 | 20.28 | 33.53 | 51.34 | 81.55 | 100.25 | |
Baseline-3 | unseen | 2.35 | 27.91 | 46.09 | 66.65 | 96.78 | 117.85 |
seen | 2.05 | 20.70 | 34.90 | 54.05 | 86.18 | 105.74 | |
Ours | unseen | 1.18 | 17.99 | 28.99 | 40.79 | 57.06 | 67.76 |
seen | 0.75 | 7.50 | 11.81 | 16.72 | 23.59 | 26.86 |
Mean Absolute Errors (cm) | |||||||
---|---|---|---|---|---|---|---|
All | ≥5 | ≥10 | ≥20 | ≥50 | ≥100 | ||
NSE | unseen | 1.56 | 23.16 | 37.49 | 53.61 | 78.09 | 97.58 |
L1 | unseen | 1.47 | 24.7 | 40.53 | 58.46 | 85.02 | 106.03 |
L2 | unseen | 1.70 | 24.6 | 39.47 | 56.74 | 83.93 | 105.55 |
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Chaudhary, P.; Leitão, J.P.; Schindler, K.; Wegner, J.D. Flood Water Depth Prediction with Convolutional Temporal Attention Networks. Water 2024, 16, 1286. https://doi.org/10.3390/w16091286
Chaudhary P, Leitão JP, Schindler K, Wegner JD. Flood Water Depth Prediction with Convolutional Temporal Attention Networks. Water. 2024; 16(9):1286. https://doi.org/10.3390/w16091286
Chicago/Turabian StyleChaudhary, Priyanka, João P. Leitão, Konrad Schindler, and Jan Dirk Wegner. 2024. "Flood Water Depth Prediction with Convolutional Temporal Attention Networks" Water 16, no. 9: 1286. https://doi.org/10.3390/w16091286