Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts
Abstract
:1. Introduction
2. Method
2.1. ANN Model for Forecasting Maximum Urban Flood Inundation
2.2. Evaluation Methods
2.3. Optimization of the ANN
2.3.1. Training Dataset
2.3.2. Training Function
- Conjugate gradient backpropagation with Fletcher–Reeves updates (CGF)
- 2.
- Conjugate gradient with Polak–Ribiére updates (CGP)
- 3.
- Conjugate gradient with Powell–Beale restarts: CGB
- 4.
- One-step secant backpropagation: OS
- 5.
- Resilient backpropagation (RP)
- 6.
- Scaled conjugate gradient backpropagation: SCG
2.4. Study Area and Real Events
3. Results
3.1. Performance on the Testing Dataset
3.2. Performance on the Real Events
4. Discussion
4.1. ANN on the Testing Dataset
4.2. ANN on the Real Events
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Set Name | Sum of RMSE (m) | Improved Grids 1 | Deteriorated Grids 2 | Improvement 3 (m) | Deterioration 4 (m) | Change Ratio 5 |
---|---|---|---|---|---|---|
RP (SD) | 50.08 | - | - | - | - | - |
RP | 44.83 | 377 (21) | 110 (3) | 6.51 | 1.41 | +10.23% |
CGB | 49.20 | 270 (14) | 217 (6) | 3.61 | 2.88 | +1.46% |
CGP | 49.70 | 264 (13) | 223 (8) | 3.390 | 3.159 | −0.46% |
CGF | 50.31 | 276 (3) | 211 (13) | 2.92 | 3.29 | −0.76% |
OSS | 51.91 | 209 (18) | 278 (12) | 2.78 | 4.75 | −3.95% |
SCG | 52.73 | 157 (7) | 330 (8) | 1.71 | 4.51 | −5.61% |
Set Name | Sum of RMSE (m) | Improved Grids 1 | Deteriorated Grids 2 | Improvement 3 (m) | Deterioration 4 (m) | Change Ratio 5 |
---|---|---|---|---|---|---|
RP (SD) | 83.94 | - | - | - | - | - |
RP | 69.68 | 229 (123) | 258 (51) | 23.79 | 9.54 | +16.99% |
CGP | 77.66 | 198 (103) | 289 (77) | 19.57 | 13.28 | +7.49% |
CGB | 81.12 | 184 (100) | 303 (90) | 18.99 | 16.16 | +3.36% |
CGF | 83.83 | 209 (105) | 278 (81) | 20.01 | 19.90 | +0.13% |
OSS | 92.25 | 164 (107) | 323 (187) | 19.94 | 28.25 | −9.89% |
SCG | 99.46 | 162 (97) | 325 (213) | 18.43 | 33.95 | −18.49% |
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Zhu, H.; Leandro, J.; Lin, Q. Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts. Water 2021, 13, 2252. https://doi.org/10.3390/w13162252
Zhu H, Leandro J, Lin Q. Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts. Water. 2021; 13(16):2252. https://doi.org/10.3390/w13162252
Chicago/Turabian StyleZhu, Hongfei, Jorge Leandro, and Qing Lin. 2021. "Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts" Water 13, no. 16: 2252. https://doi.org/10.3390/w13162252
APA StyleZhu, H., Leandro, J., & Lin, Q. (2021). Optimization of Artificial Neural Network (ANN) for Maximum Flood Inundation Forecasts. Water, 13(16), 2252. https://doi.org/10.3390/w13162252