Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models
Abstract
:1. Introduction
2. Study Area and Materials
3. Forecast Models
3.1. Simulation Model
- (1)
- Observed distribution: 8 real events (rainfall data).
- (2)
- Synthetic distribution: a total of 12 designed events were generated, based on different combinations of rainfall durations (12, 24, 48 and 72 h) and return periods (20, 50 and 100 years) at main gauge stations.
3.2. ANN-Based Models
3.2.1. Self-Organizing Map (SOM)
3.2.2. Recurrent Configuration of Nonlinear Autoregressive with Exogenous Inputs (RNARX)
3.3. ANNs-Based Regional Inundation Depth Forecasting
3.4. Evaluation Indexes
4. Results and Discussion
4.1. SOM Clustering
4.2. Forecasting Average Regional Inundation Depths (ARID)
4.3. ANNs-Based Models for Forecasting Regional Inundation Maps
4.4. Development of the Flood Early Warning System
5. Conclusions
- (1)
- The input datasets for the SOM consist of high-dimensional spatial inundation depths (with a grid resolution of 75 m × 75 m) of the study area obtained from the 2-D simulation model based on a number of real storm events. The main features of the spatial inundation distributions can be well distinguished by an SOM with 4 × 4 neurons to obtain a distinguishable topology. The SOM network can effectively cluster the high-dimensional (10,744 grids) inundation depths to extract and present their topological structures.
- (2)
- The results suggest that the RNARX network configured with current regional rainfall information and the model’s recurrent output can well capture the main features of the input-output patterns to provide stable and reliable forecasts of ARIDs.
- (3)
- The proposed model integrates the favorable essence of both networks (SOM & RNARX) and fuses their corresponding results to provide real-time visible regional multi-step-ahead flood inundation maps with high resolution; their nowcasts are reliable and adequate (with small RMSE and high R2 values).
- (4)
- Regarding the execution efficiency of the developed system for the study area, the system can very quickly (in just a few seconds) carry out three to twelve-hour-ahead forecasting of area-wide inundation maps and thereby lead to real-time flood forecasting.
Author Contributions
Finanzierung
Acknowledgments
Conflicts of Interest
References
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Event | Beginning (yy/mm/dd) | Ending (yy/mm/dd) | Duration (h) | Maximum Flow (m3/s) | Accumulated Average Rainfall (mm) | Maximum Average Inundation Depth (m) | ||
---|---|---|---|---|---|---|---|---|
St*. #4131453 | St. #4232401 | St. #4332401 | ||||||
1 | 2001/12/20, 5:00 a.m. | 2001/12/27, 9:00 a.m. | 172 | 731 | 814 | 84 | 453 | 1.70 |
2 | 2003/12/5, 12:00 p.m. | 2003/12/12, 9:00 a.m. | 165 | 554 | 433 | 183 | 396 | 1.67 |
3 | 2006/12/20, 2:15 a.m. | 2006/12/26, 8:30 a.m. | 150 | 534 | 207 | 87 | 268 | 1.48 |
4 | 2008/12/31, 19:30 a.m. | 2009/1/8, 7:30 a.m. | 180 | 478 | 487 | 96 | 390 | 1.74 |
5 | 2012/1/10, 7:15 a.m. | 2012/1/17, 4:00 p.m. | 177 | 453 | 506 | 39 | 255 | 1.70 |
6 | 2012/12/13, 0:15 a.m. | 2013/1/13, 0:00 a.m. | 744 | 509 | 920 | 75 | 973 | 3.28 |
7 | 2013/11/28 9:30 p.m. | 2013/12/6, 9:30 p.m. | 192 | 745 | 1142 | 63 | 996 | 4.17 |
8 | 2014/12/13, 12:00 p.m. | 2015/1/1, 4:00 p.m. | 460 | 634 | 801 | 57 | 1653 | 3.35 |
Dataset | Real Events | Designed Events |
---|---|---|
Training | Event 3 Event 5 Event 6 Event 7 | 100yearreturnperiod_12hourRainfall 50yearreturnperiod_24hourRainfall 20yearreturnperiod_48hourRainfall 50yearreturnperiod_48hourRainfall 20yearreturnperiod_72hourRainfall 100yearreturnperiod_72hourRainfall |
Validation | Event 2 Event 4 | 20yearreturnperiod_12hourRainfall 20yearreturnperiod_24hourRainfall 100yearreturnperiod_48hourRainfall |
Testing | Event 8 | 50yearreturnperiod_12hourRainfall 100yearreturnperiod_24hourRainfall 50yearreturnperiod_72hourRainfall |
Input Factors | Kemaman River Average Rainfall | Cherul River Average Rainfall | Flood Depth | |
---|---|---|---|---|
Forecast Time-Step | ||||
T + 1 (3 h-ahead) | T − 2 | T − 2 | - | |
T + 2 (6 h-ahead) | T − 1 | T − 1 | - | |
T + 3 (9 h-ahead) | T | T | - | |
T + 4 (12 h-ahead) | T | T | T + 3 |
Forecasting Time-Step | RMSE (m) | |||||
---|---|---|---|---|---|---|
Training | Validation | Testing | Training | Validation | Testing | |
3 h-ahead | 0.28 | 0.30 | 0.34 | 0.92 | 0.90 | 0.90 |
6 h-ahead | 0.28 | 0.30 | 0.34 | 0.92 | 0.90 | 0.90 |
9 h-ahead | 0.29 | 0.30 | 0.33 | 0.91 | 0.90 | 0.90 |
12 h-ahead | 0.31 | 0.34 | 0.35 | 0.90 | 0.87 | 0.89 |
Time (h) | Alle | 0–1 m | 1–2 m | 2–3 m | >3 m |
---|---|---|---|---|---|
T = 75 | 10,744 | 7274 | 1361 | 1231 | 908 |
T = 81 | 10,744 | 6530 | 1617 | 1376 | 1251 |
T = 87 | 10,744 | 4488 | 2176 | 1964 | 2146 |
T = 93 | 10,744 | 3466 | 2053 | 2452 | 2803 |
T = 108 | 10,744 | 1400 | 1195 | 2473 | 5706 |
T = 120 | 10,744 | 614 | 780 | 1707 | 7673 |
Time (h) | Time Step | Alle | 0–1 m | 1–2 m | 2–3 m | >3 m | |
---|---|---|---|---|---|---|---|
RMSE * | R2 | RMSE | RMSE | RMSE | RMSE | ||
T = 75 | T + 1 (=3 h) | 0.45 | 0.94 | 0.52 | 0.38 | 0.16 | 0.09 |
T + 2 (=6 h) | 0.44 | 0.94 | 0.51 | 0.36 | 0.14 | 0.10 | |
T + 3 (=9 h) | 0.45 | 0.94 | 0.52 | 0.38 | 0.17 | 0.09 | |
T + 4 (=12 h) | 0.49 | 0.94 | 0.55 | 0.45 | 0.25 | 0.17 | |
T = 81 | T + 1 | 0.65 | 0.97 | 0.53 | 0.79 | 0.81 | 0.84 |
T + 2 | 0.64 | 0.97 | 0.52 | 0.77 | 0.79 | 0.81 | |
T + 3 | 0.66 | 0.97 | 0.53 | 0.79 | 0.82 | 0.85 | |
T + 4 | 0.68 | 0.97 | 0.54 | 0.81 | 0.85 | 0.90 | |
T = 87 | T + 1 | 0.38 | 0.99 | 0.36 | 0.45 | 0.40 | 0.34 |
T + 2 | 0.37 | 0.99 | 0.35 | 0.43 | 0.39 | 0.31 | |
T + 3 | 0.39 | 0.99 | 0.36 | 0.45 | 0.41 | 0.35 | |
T + 4 | 0.08 | 1 | 0.06 | 0.08 | 0.08 | 0.12 | |
T = 93 | T + 1 | 0.19 | 1 | 0.16 | 0.2 | 0.2 | 0.19 |
T + 2 | 0.19 | 1 | 0.10 | 0.16 | 0.19 | 0.19 | |
T + 3 | 0.19 | 1 | 0.16 | 0.21 | 0.21 | 0.19 | |
T + 4 | 0.12 | 1 | 0.09 | 0.12 | 0.13 | 0.12 | |
T = 108 | T + 1 | 0.25 | 1 | 0.11 | 0.07 | 0.12 | 0.32 |
T + 2 | 0.25 | 1 | 0.11 | 0.07 | 0.12 | 0.32 | |
T + 3 | 0.24 | 1 | 0.11 | 0.07 | 0.11 | 0.31 | |
T + 4 | 0.18 | 1 | 0.12 | 0.08 | 0.08 | 0.23 | |
T = 120 | T + 1 | 0.42 | 1 | 0.48 | 0.57 | 0.34 | 0.42 |
T + 2 | 0.42 | 1 | 0.48 | 0.57 | 0.34 | 0.42 | |
T + 3 | 0.42 | 1 | 0.48 | 0.57 | 0.34 | 0.42 | |
T + 4 | 0.34 | 1 | 0.49 | 0.61 | 0.43 | 0.34 |
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Chang, L.-C.; Amin, M.Z.M.; Yang, S.-N.; Chang, F.-J. Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models. Water 2018, 10, 1283. https://doi.org/10.3390/w10091283
Chang L-C, Amin MZM, Yang S-N, Chang F-J. Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models. Water. 2018; 10(9):1283. https://doi.org/10.3390/w10091283
Chicago/Turabian StyleChang, Li-Chiu, Mohd Zaki M. Amin, Shun-Nien Yang, and Fi-John Chang. 2018. "Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models" Water 10, no. 9: 1283. https://doi.org/10.3390/w10091283