Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors
Abstract
:1. Introduction
2. Methodology
2.1. Model Concept
2.2. Simulation of Rainfall-Induced Inundation Events
2.3. Identification of the Virtual IoT (VIOT) Grids
2.4. Artificial Neural Network Model Associated with Multiple Transfer Functions
2.5. Integration with Real-Time Correction Approach
2.6. Model Framework
2.6.1. Conceptual Model
2.6.2. Actual Model
3. Study Area and Data
4. Results and Discussions
4.1. Simulation of Rainfall-Induced Inundation
4.1.1. Extraction of Gridded Rainstorms
4.1.2. Simulation of Rainfall-Induced Inundation Events
4.2. Identification of Ungauged Locations as VIOT Grids
4.3. Training of ANN-Derived Model
4.4. Model Validation
4.4.1. Extraction of Validation Data
4.4.2. Evaluation of the Inundation-Depth Estimates
4.4.3. Assessment of the Flooding Area
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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No of Formula | Formula |
---|---|
1 | |
2 | |
3 | +1 |
4 | |
5 | |
6 |
Transfer Function | Formula | Derivative | |
---|---|---|---|
TF1 | Logistic(soft step, Sigmoid) | ||
TF2 | Tanh | ||
TF3 | Arctan | ||
TF4 | Identity | x | |
TF5 | Rectified linear unit (ReLU) | ||
TF6 | Parameteric rectified linear unit (PReLU, leaky ReLU) | ||
TF7 | Exponential linear unit(ELU) | ||
TF8 | Inverse abs (IA) | ||
TF9 | Rootsig (RS) | ||
TF10 | Sech function (SF) |
Function | Facilities | Number |
---|---|---|
Hydraulic analysis | Sub-basins | 4731 |
Cross-sections | 9838 | |
Gates | 62 | |
Bridges | 9018 | |
Sewer | 68.6 km | |
Maintenance of sewer system | 1382 | |
Hydrological analysis | Rainfall-runoff node | 4097 |
Parameters | Definition | ||
---|---|---|---|
Transfer functions used | TF1-TF10 | ||
Input factors | Resulting areal average of Inundation depth from IoT sensors | ||
Output factor | Inundation depth at VIOT grids | ||
Number of hidden levels | 1 | ||
Number of neurons | 3 | ||
Calibration of parameters of transfer function | Number of optimizations | 10 | |
Mean | 1 | ||
Standard deviation | 3 | ||
Mean | 0 | ||
Standard deviation | 1 | ||
Mean | 1 | ||
Standard deviation | 0.005 |
Transfer Function | No of Optimization | Adjust Factor | 1.00113 | |||||
---|---|---|---|---|---|---|---|---|
1 | OPT1 | Weights of | The 1st hidden layer | Input factors | ||||
1 | Bias | |||||||
Neuron | 1 | 2.27251 | −1.14272 | |||||
2 | 0.58986 | −2.71961 | ||||||
3 | 4.56974 | −4.28293 | ||||||
Output layer | The 1st hidden layer | |||||||
1 | 2 | 3 | Bias | |||||
Input factor | 1 | −0.87316 | 1.04999 | 0.08291 | −0.21039 |
Performance Index | Inundation Depth | ||||
Average | Maximum | VIOT1676 | VIOT6655 | VIOT2978 | |
Root mean square error RMSE (m) | 0.103 | 0.015 | 0.002 | 0.000 | 0.000 |
Coefficient of determination (R2) | 0.891 | 0.703 | 0.993 | 1.000 | 1.000 |
Performance Index | Precision Index | Recall Index | F1 | RMSE (km2) | R2 | |
---|---|---|---|---|---|---|
Statistical properties | Mean | 0.669 | 0.364 | 0.461 | 0.161 | 0.65 |
Standard deviation | 0.209 | 0.220 | 0.208 | |||
Maximum | 1.000 | 1.000 | 1.000 | |||
Minimum | 0.000 | 0.000 | 0.000 |
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Wu, S.-J.; Hsu, C.-T.; Shen, J.-C.; Chang, C.-H. Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors. Water 2022, 14, 2189. https://doi.org/10.3390/w14142189
Wu S-J, Hsu C-T, Shen J-C, Chang C-H. Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors. Water. 2022; 14(14):2189. https://doi.org/10.3390/w14142189
Chicago/Turabian StyleWu, Shiang-Jen, Chih-Tsu Hsu, Jhih-Cyuan Shen, and Che-Hao Chang. 2022. "Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors" Water 14, no. 14: 2189. https://doi.org/10.3390/w14142189
APA StyleWu, S.-J., Hsu, C.-T., Shen, J.-C., & Chang, C.-H. (2022). Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors. Water, 14(14), 2189. https://doi.org/10.3390/w14142189