A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River
Abstract
:1. Introduction
- Develop a new state-of-the-art model for flooding forecasting, allowing for a more precise and accurate early flooding alert system.
- Verify the addition of spatiotemporal data for improved flooding forecasting results.
- Development of a reliable ML model based on graph theory and DL paradigm.
2. Proposed Model
2.1. Persistence Model
2.2. GNN-SAGE and GNN-ResGated Models
2.3. SHAP Analysis
3. Validation and Analysis of Results
3.1. Humber River Description
3.2. Evaluation Metrics
3.3. Dataset Size Evaluation
3.4. Results for a 1 h Forecast Horizon
3.5. Results for a 3 h Forecast Horizon
3.6. Results for a 6 h Forecast Horizon
3.7. Results for a 12 h Forecast Horizon
3.8. Results for a 24 h Forecast Horizon
3.9. Results of the SHAP Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Metric | 1 h Ahead | 3 h Ahead | 6 h Ahead | 12 h Ahead | 24 h Ahead |
---|---|---|---|---|---|
RMSE (m) | 0.02516 | 0.06736 | 0.09200 | 0.12215 | 0.16077 |
MAE (m) | 0.01592 | 0.0345 | 0.04372 | 0.05514 | 0.07077 |
MAPE | 1.04% | 2.18% | 2.65% | 3.20% | 3.78% |
R2 | 96.45% | 75.50% | 59.10% | 40.12% | 22.65% |
Model | Metric Value | Author |
---|---|---|
Spatio-temporal attention LSTM (STA-LSTM) | Error rate 3.96% for 6 h forecasting horizon 3.98% for 12 h forecasting horizon 6.31% for 24 h forecasting horizon | Zhang et al. [19] |
Quantitative precipitation forecast (QPF) | RMSE 0.09 m for 1 h forecasting horizon | Wu et al. [28] |
Support vector machine (SVM) | RMSE (MAE) 0.072 m (0.036 m) for 3 h forecasting horizon 0.131 m (0.070 m) for 6 h forecasting horizon | Dazzi et al. [80] |
Support vector regression (SVR) | RMSE 0.07 m for 1 h forecasting horizon 0.25 m for 3 h forecasting horizon RMSE | Nguyen and Chen [82] |
Multiple additive regression trees (MART) | 0.14 m for 1 h forecasting horizon 0.29 m for 3 h forecasting horizon | Fu et al. [83] |
Hybrid wavelet and ANN (WANN) | RMSE (R2) 0.03 m (98%) for 1 h forecasting horizon 0.038 m (97%) for 3 h forecasting horizon 0.12 m (60%) for 6 h forecasting horizon | Alexander et al. [84] |
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Oliveira Santos, V.; Costa Rocha, P.A.; Scott, J.; Thé, J.V.G.; Gharabaghi, B. A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River. Water 2023, 15, 1827. https://doi.org/10.3390/w15101827
Oliveira Santos V, Costa Rocha PA, Scott J, Thé JVG, Gharabaghi B. A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River. Water. 2023; 15(10):1827. https://doi.org/10.3390/w15101827
Chicago/Turabian StyleOliveira Santos, Victor, Paulo Alexandre Costa Rocha, John Scott, Jesse Van Griensven Thé, and Bahram Gharabaghi. 2023. "A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River" Water 15, no. 10: 1827. https://doi.org/10.3390/w15101827
APA StyleOliveira Santos, V., Costa Rocha, P. A., Scott, J., Thé, J. V. G., & Gharabaghi, B. (2023). A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River. Water, 15(10), 1827. https://doi.org/10.3390/w15101827