An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Dataset
2.2.1. Flood Inventory
2.2.2. Conditioning Factors
2.3. Research Framework
2.4. Non-Flood Sampling Approach
2.4.1. Random Sampling
2.4.2. Stratified Sampling
2.4.3. Inverse-Occurrence Sampling
2.4.4. Sampled Non-Flood Data
2.5. Learning Technique
2.5.1. Support Vector Machine (SVM)
2.5.2. Random Forest (RF)
2.5.3. Artificial Neural Network (ANN)
2.5.4. Convolutional Neural Networks (CNNs)
2.6. Model Assessment
3. Results
3.1. Sampling Approach Comparison
3.2. Learning Technique Comparison
3.3. Interaction between Sampling Approach and Learning Technique
3.4. Flood Mechanisms
3.5. Flood Susceptibility
3.5.1. Flood Density Order
3.5.2. Flood Density Outlier
3.5.3. Flood Susceptibility Map
4. Discussion
4.1. Flood Susceptibility Learning in Underreported Areas
4.2. Flood Susceptibility Model Evaluation
4.3. Uncertainties and Limitations
4.4. Benefits and Future Work
5. Conclusions
- (1)
- Sampling approaches have a greater impact on model performance than learning techniques. The AUC variations caused by learning techniques ranged from 0.04 to 0.09, whereas the AUC variations caused by sampling approaches were between 0.15 and 0.22 and were all larger than 0.1.
- (2)
- The inverse-occurrence sampling approach representing spatial dependence outperformed the two other commonly used sampling approaches, not only for high AUC values but also for small AUC variations. This finding is robust in regard to multiple learning techniques and different flooding mechanisms. AUCs in the inverse group have a narrower range, that is, 0.14–0.18 in Tianhe, and 0.35–0.39 in Panyu, than in the random group, that is, 0.24–0.28 in Tianhe, and 0.43–0.53 in Panyu; and the stratified group, which was 0.23–0.30 in Tianhe, and 0.42–0.48 in Panyu.
- (3)
- The most accurate learning technique measured by the AUC was CNN-RF, followed by SVM, CNN-SVM, RF, CNN, and ANN.
- (4)
- Flood density order and outliers should be applied to assess the quality of flood susceptibility models. ANN- and CNN-based models tended to produce polarized patterns in flood susceptibility maps, contradicting the ascending order of flood density with increasing susceptibility levels. Moreover, flood density outliers tended to appear in the models derived using RF and CNN-RF.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Study Area | Flood Records | Area (km2) | Flood Density (1/km2) | Cluster Analysis (p-Value) | Population 2021 (Million) | GDP 2021 (Billion Yuan) | Average Slope (Degree) |
---|---|---|---|---|---|---|---|
Tianhe | 238 | 158.35 | 1.50 | 0.00 | 2.24 | 601.2 | 9.02 |
Panyu | 80 | 516.34 | 0.15 | 0.41 | 2.82 | 265.4 | 4.55 |
Data | Source | Format | Resolution |
---|---|---|---|
Stream network | Open street map | Polyline, shape file | - |
Land use | SinoLC-1, 2023 | Raster | 1 m |
DEM | ASTER GDEM | Raster | 30 m |
Remote sensing imagery | Landsat 8, 2020 | Raster | 30 m |
Gaofen 1, 2020 | Raster | 8 m |
Clusters | Flood Number | Flood Percentage (%) | Area Percentage (%) | Non-Flood Number (Inverse) | Non-Flood Number (Stratified) | |
---|---|---|---|---|---|---|
Tianhe | 1 | 171 | 71.8 | 40.8 | 17 | 97 |
2 | 58 | 24.4 | 33.2 | 45 | 79 | |
3 | 8 | 3.4 | 8.4 | 57 | 20 | |
4 | 1 | 0.4 | 10.5 | 59 | 25 | |
5 | 0 | 0.0 | 7.1 | 60 | 17 | |
Panyu | 1 | 44 | 55.0 | 18.3 | 9 | 14 |
2 | 27 | 33.8 | 23.4 | 13 | 19 | |
3 | 7 | 8.8 | 22.8 | 18 | 18 | |
4 | 1 | 1.3 | 20.8 | 20 | 17 | |
5 | 1 | 1.3 | 14.8 | 20 | 12 |
Method | Tianhe | Panyu | Average Rank | AUC Change S | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Random | Stratified | Inverse | Rank | Random | Stratified | Inverse | Rank | |||
SVM | 0.6974 | 0.7037 | 0.9018 | 4.0 | 0.7828 | 0.7824 | 0.8392 | 1.0 | 2.5 | 0.2044 |
RF | 0.6949 | 0.6983 | 0.9114 | 4.0 | 0.7625 | 0.7579 | 0.8243 | 3.0 | 3.5 | 0.2165 |
ANN | 0.6636 | 0.6653 | 0.8760 | 6.0 | 0.7186 | 0.7164 | 0.7827 | 6.0 | 6.0 | 0.2124 |
CNN | 0.7359 | 0.7382 | 0.8846 | 3.7 | 0.7510 | 0.7470 | 0.8047 | 5.0 | 4.3 | 0.1487 |
CNN-SVM | 0.7403 | 0.7420 | 0.9065 | 2.3 | 0.7608 | 0.7565 | 0.8155 | 4.0 | 3.2 | 0.1662 |
CNN-RF | 0.7543 | 0.7583 | 0.9176 | 1.0 | 0.7748 | 0.7737 | 0.8274 | 2.0 | 1.5 | 0.1633 |
AUC change L | 0.0907 | 0.0930 | 0.0416 | — | 0.0642 | 0.0660 | 0.0565 | — | — | — |
Tianhe | Panyu | ||
---|---|---|---|
ISP | 1000 | ISP | 1000 |
NDVI | 1000 | Elevation | 461 |
Rainfall | 358 | NDVI | 436 |
Elevation | 41 | Rainfall | 92 |
DD | 40 | DD | 81 |
DR | 0 | DR | 46 |
NDBI | 0 | NDBI | 18 |
RE | 0 | STDE | 12 |
Slope | 0 | RE | 10 |
SPI | 0 | Slope | 8 |
STDE | 0 | SPI | 1 |
TWI | 0 | TWI | 1 |
Study Area | Sampling Approach | SVM | RF | ANN | CNN | CNN-SVM | CNN-RF |
---|---|---|---|---|---|---|---|
Tianhe | Random | √ | √ | × | √ | √ | √ |
Stratified | √ | √ | × | × | √ | √ | |
Inverse | √ | √ | × | √ | √ | √ | |
Panyu | Random | √ | √ | × | × | √ | √ |
Stratified | √ | √ | × | √ | √ | √ | |
Inverse | √ | √ | × | × | √ | √ |
Study Area | Sampling Approach | SVM | RF | ANN | CNN | CNN-SVM | CNN-RF |
---|---|---|---|---|---|---|---|
Tianhe (1.5/km2) | Random | — | 1 (7.89) | — | — | — | 1 (239.2) |
Stratified | — | 1 (17.82) | — | — | — | 2 (7.27, 575.05) | |
Inverse | — | — | — | — | — | — | |
Panyu (0.15/km2) | Random | — | — | — | — | — | 1 (262.55) |
Stratified | — | 1 (8.18) | — | — | — | 1 (848.8) | |
Inverse | — | — | — | — | — | 1 (13.61) |
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Wang, C.; Lin, Y.; Tao, Z.; Zhan, J.; Li, W.; Huang, H. An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping. Remote Sens. 2023, 15, 5384. https://doi.org/10.3390/rs15225384
Wang C, Lin Y, Tao Z, Zhan J, Li W, Huang H. An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping. Remote Sensing. 2023; 15(22):5384. https://doi.org/10.3390/rs15225384
Chicago/Turabian StyleWang, Changpeng, Yangchun Lin, Zhiwen Tao, Jiayin Zhan, Wenkai Li, and Huabing Huang. 2023. "An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping" Remote Sensing 15, no. 22: 5384. https://doi.org/10.3390/rs15225384