Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Physically Based Model Used as Reference
3.2. Self-Organizing Map (SOM)
3.3. Recurrent Network
3.3.1. Phase Space Composition
3.3.2. Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX)
3.4. Update SOMs with Associated Variables
3.5. Hybrid Model Structure
3.6. Train/Validation and Test Datasets
3.7. Evaluation Metrics
4. Results and Discussion
4.1. Selected Rainfall-Runoff Events
4.2. Assessment of the SOM Models Trained
4.3. Assessment of the NARX Models Trained
4.4. Assessment of the Hybrid Model
4.4.1. Global Performance
4.4.2. Performance on Selected Events
4.5. Runtime Comparison
4.6. Contraints and Limitations of the Methodology
5. Conclusions and Future Works
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Acronyms | Meaning |
AID | Average inundation depth |
CMMF | Conditional Median/Mean Function |
CSI | Critical Success Index |
DEM | Digital Elevation Model |
DRB | Don River Basin |
EP | Estimated probability |
NARX | Nonlinear Autoregressive neural network with exogenous inputs |
POD | Probability of detection |
POI | Point of interest |
RE-O | Rainfall Events—Observations |
RE-OH | Rainfall Events—Observations (Highest) |
RE-SS | Rainfall Events—Synthetic by Shuffling |
RE-SE | Rainfall Events—Synthetic by Extrapolation |
RMSE | Root mean square error |
SOM | Self-Organizing Maps |
SR | Success Ratio |
SWMM | Storm Water Management Model |
TN | Topological node |
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Acronym | Description | Number of Events |
---|---|---|
RE-O | Rainfall Events—Observations | 87 |
RE-OH | Rainfall Events—Observations (Highest) | 10 |
RE-SS | Rainfall Events—Synthetic by Shuffling | 87 |
RE-SE | Rainfall Events—Synthetic by Extrapolation | 10 |
Dataset | Events | Total P (mm) | Peak Q (m3/s) | Peak AID (m) | |||
---|---|---|---|---|---|---|---|
Min | Max | Min | Max | Min | Max | ||
Train/validation | 17 | 84.1 | 238.6 | 83.6 | 294.9 | 0.13 | 1.39 |
Test | 13 | 95.2 | 246.9 | 64.1 | 274.9 | 0.06 | 1.52 |
Metric | Water Depth (cm) | Lead Time (minutes) | |||||||
---|---|---|---|---|---|---|---|---|---|
0 | 30 | 60 | 90 | 120 | 150 | 180 | 240 | ||
POD | 10 | 0.97 | 0.97 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 1.00 |
25 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | |
50 | 0.97 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | |
100 | 0.91 | 0.93 | 0.95 | 0.96 | 0.97 | 0.97 | 0.97 | 0.97 | |
SR | 10 | 0.86 | 0.87 | 0.89 | 0.86 | 0.80 | 0.73 | 0.68 | 0.65 |
25 | 0.93 | 0.85 | 0.85 | 0.85 | 0.85 | 0.83 | 0.79 | 0.70 | |
50 | 0.94 | 0.84 | 0.79 | 0.75 | 0.73 | 0.71 | 0.68 | 0.61 | |
100 | 0.99 | 0.98 | 0.97 | 0.94 | 0.85 | 0.81 | 0.79 | 0.76 | |
CSI | 10 | 0.83 | 0.85 | 0.87 | 0.85 | 0.80 | 0.72 | 0.68 | 0.65 |
25 | 0.91 | 0.84 | 0.84 | 0.84 | 0.85 | 0.83 | 0.79 | 0.69 | |
50 | 0.91 | 0.82 | 0.78 | 0.74 | 0.72 | 0.70 | 0.68 | 0.60 | |
100 | 0.90 | 0.91 | 0.92 | 0.91 | 0.83 | 0.78 | 0.77 | 0.75 |
Metric | Water Depth (cm) | Lead Time (minutes) | |||||||
---|---|---|---|---|---|---|---|---|---|
0 | 30 | 60 | 90 | 120 | 150 | 180 | 240 | ||
POD | 10 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
25 | 0.97 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 | |
50 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
100 | 0.97 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | |
SR | 10 | 0.95 | 0.90 | 0.81 | 0.74 | 0.71 | 0.68 | 0.66 | 0.58 |
25 | 1.00 | 0.97 | 0.95 | 0.88 | 0.82 | 0.79 | 0.75 | 0.70 | |
50 | 0.99 | 0.94 | 0.90 | 0.87 | 0.82 | 0.74 | 0.71 | 0.68 | |
100 | 0.97 | 0.89 | 0.85 | 0.78 | 0.71 | 0.64 | 0.60 | 0.58 | |
CSI | 10 | 0.95 | 0.89 | 0.81 | 0.74 | 0.71 | 0.68 | 0.66 | 0.58 |
25 | 0.97 | 0.96 | 0.94 | 0.88 | 0.81 | 0.78 | 0.75 | 0.69 | |
50 | 0.99 | 0.94 | 0.90 | 0.87 | 0.82 | 0.74 | 0.71 | 0.68 | |
100 | 0.95 | 0.89 | 0.85 | 0.78 | 0.71 | 0.64 | 0.60 | 0.58 |
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Zanchetta, A.D.L.; Coulibaly, P. Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow. Forecasting 2022, 4, 126-148. https://doi.org/10.3390/forecast4010007
Zanchetta ADL, Coulibaly P. Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow. Forecasting. 2022; 4(1):126-148. https://doi.org/10.3390/forecast4010007
Chicago/Turabian StyleZanchetta, Andre D. L., and Paulin Coulibaly. 2022. "Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow" Forecasting 4, no. 1: 126-148. https://doi.org/10.3390/forecast4010007
APA StyleZanchetta, A. D. L., & Coulibaly, P. (2022). Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow. Forecasting, 4(1), 126-148. https://doi.org/10.3390/forecast4010007