Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier
Abstract
:1. Introduction
2. Materials and Methods
2.1. Case Study
2.2. Materials
2.2.1. Data
Data | Format | Data Sources |
---|---|---|
Flooding points | Shapefile | Water Resources Department of Guangdong Province, China (http://swj.gz.gov.cn (accessed on 15 March 2021)) Drainage Services Department, Hong Kong (https://www.dsd.gov.hk (accessed on 19 March 2021)) Municipal Affairs Bureau, Macau (http://www.iam.gov.mo (accessed on 20 March 2021)) TouTiao (https://www.toutiao.com (accessed on 25 March 2021)) |
Digital elevation model | Raster | Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) 30 m |
Waterway network | Shapefile | OpenStreetMap (https://www.openhistoricalmap.org (accessed on 24 February 2021)) |
Road network | Shapefile | OpenStreetMap (https://www.openhistoricalmap.org (accessed on 26 February 2021)) |
Fractional vegetation cover | Tif | Landsat 8 Operational Land Imager_Thermal Infrared Sensor |
Soil type | Raster | Resource and Environment Science and Data Center, China (https://www.resdc.cn (accessed on 27 February 2021)) |
Impervious surface percentage | Raster | [49] |
2.2.2. Spatial Distribution of the Driving Factors
- (1)
- Fractional Vegetation Cover
- (2)
- Soil Water Retention
- (3)
- Impervious surface percentage
2.3. Methodology
2.3.1. Risk Assessment Based on Weighted Naive Bayes
- (1)
- The determination of weight
- (2)
- The generation of conditional probability tables
- (3)
- The calculation of risk likelihood
2.3.2. Spatial Urban Flooding Assessment within the Framework of a Complex System
- (1)
- Proximity matrix analysis
- (2)
- Modularization analysis
- (3)
- Contribution analysis
3. Results and Discussion
3.1. Weightings and the Best-Estimated Conditional Probability Tables
3.2. Mapping of Urban Flooding Risk
3.3. Dominant Risk Attributes Analysis
3.3.1. Multi-Factor-Driven Cluster
3.3.2. Single-Factor-Driven Cluster
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | DEM | SLOP | DW | RD | FVC | SWR | ISP |
---|---|---|---|---|---|---|---|
AHP | 5.0% | 5.3% | 8.6% | 8.8% | 28.7% | 12.7% | 30.8% |
EW | 0.3% | 0.3% | 0.2% | 6.4% | 46.9% | 0.4% | 45.5% |
Linear weighting | 2.6% | 2.8% | 4.4% | 7.6% | 37.9% | 6.6% | 38.1% |
Class | Low Risk | Medium Risk | High Risk |
---|---|---|---|
Risk range | 0–23.0% | 23.0–43.7% | >43.7% |
Area (hm2) | 3212.9 | 5776.3 | 3106.4 |
Proportion (%) | 26.6% | 47.5% | 25.7% |
Multi-Factor-Driven | Single-Factor-Driven | ||||||||
---|---|---|---|---|---|---|---|---|---|
Artificial Factor | Topographic Factor | Artificial Factor | Topographic Factor | ||||||
Driving factor | RD, FVC, ISP | RD, ISP | SWR | DW, FVC, SWR | FVC, ISP | DEM, SLOP, DW | DEM, SLOP | RD | SLOP |
Cluster | A-2, C-1 | B-1, D-1 | B-4 | C-4, D-4 | D-2 | A-3, B-2 | B-3, C-3 | A-1, C-2 | D-3 |
Quantity | 214 | 255 | 91 | 90 | 16 | 441 | 163 | 158 | 20 |
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Wang, M.; Fu, X.; Zhang, D.; Chen, F.; Su, J.; Zhou, S.; Li, J.; Zhong, Y.; Tan, S.K. Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier. Sustainability 2023, 15, 5740. https://doi.org/10.3390/su15075740
Wang M, Fu X, Zhang D, Chen F, Su J, Zhou S, Li J, Zhong Y, Tan SK. Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier. Sustainability. 2023; 15(7):5740. https://doi.org/10.3390/su15075740
Chicago/Turabian StyleWang, Mo, Xiaoping Fu, Dongqing Zhang, Furong Chen, Jin Su, Shiqi Zhou, Jianjun Li, Yongming Zhong, and Soon Keat Tan. 2023. "Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier" Sustainability 15, no. 7: 5740. https://doi.org/10.3390/su15075740
APA StyleWang, M., Fu, X., Zhang, D., Chen, F., Su, J., Zhou, S., Li, J., Zhong, Y., & Tan, S. K. (2023). Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier. Sustainability, 15(7), 5740. https://doi.org/10.3390/su15075740