Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China
Abstract
:1. Introduction
- (1)
- Can the optimization of impervious surface space layout reduce surface runoff? How can it be achieved?
- (2)
- Does ACO-SCS based on geographic simulation technology realize the optimization of impervious surface space layout? What is the effect of optimization?
- (3)
- How does the optimization result serve urban renewal planning for urban rainstorm waterlogging prevention?
2. Materials and Methods
2.1. Study Area
2.2. Data and Preprocessing
2.3. Optimization Model of Impervious Surface Space Layout
2.3.1. SCS-CN
2.3.2. Ant Colony Optimization
Probability Function
Heuristic Function
The Generation of Ant Colony
Suitability Evaluation of Ants
Update Operation of Pheromone Concentration
Parameter Settings
Integration of Ant Colony Optimization and Soil Conservation Service Curve Number Model
- (1)
- Set the initial parameters of the algorithm, including the heuristic factor, the expecting factor, the initial pheromone, the volatilization factor, the number of iterations, and the constant coefficient.
- (2)
- Configuration process of impervious surfaces: The heuristic function and the probability function are calculated according to the pheromone. The runoff plot is used as an optimization unit. Each grid cell of each ant was configured with an impervious surface type in turn. Prohibited regions and the number of impervious surface types constrain the optimization regions and areas.
- (3)
- Suitability evaluation of the ants: Impervious surfaces with completed spatial configuration are input as a parameter into SCS-CN to calculate the surface runoff (i.e., the suitability of the ant). The top ten ants with the best suitability were selected. Compared with the optimum impervious surface space layout of the previous iteration, the optimum impervious surface space is selected.
- (4)
- Update operation of pheromone concentration: The increment and volatility of pheromones were calculated according to the top ten ants with the current optimum suitability, and then the pheromone concentration was updated.
- (5)
- Condition for stopping the algorithm: If the current iteration number reaches the maximum iteration number, the algorithm ends. The current optimal space layout of impervious surfaces is the final result. Instead, repeat step 2 until the maximum number of iterations is reached. When iteration stops, runoff coefficients before and after optimization are calculated by the SCS-CN model, and the optimization rate is obtained by comparison.
- (6)
- Adjustment of the area of impervious surface: First, the increment of the runoff coefficient of each grid element after iteration is calculated and sorted. Second, the impervious surface type corresponding to the grid cell with the largest increment of runoff coefficient remains unchanged. The adjusted impervious surface area is calculated and compared to the initial impervious area. If the result is not lower than the limited area, the adjusted impervious surfaces repeat the above operation until the area is closest to the standard.
2.4. Landscape Pattern Index
3. Results
3.1. Comparison of Impervious Surface Changes after Optimization
3.2. Landscape Pattern Change
4. Discussion
4.1. Evaluation of Optimization Model
4.2. The Influence of Various Factors on the Optimization of Impervious Surface Space Layout
4.2.1. The Influence of Input Factors on the Optimization of Impervious Surface Space Layout
4.2.2. The Influence of Initial Impervious Surface Space Layout on the Optimization Model
Relationship between Optimization Rate and Proportion of Impervious Surface Type
Relationship Between Optimization Rate and Space Layout of the Initial Impervious Surfaces
4.3. Practical Significance of Optimal results
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data | Format | Time | Source |
---|---|---|---|
1:4 Million administrative divisions | Esri shapefile | 2005 | National Geomatics Center of China |
Landsat remote-sensing image | Img | 28-10-2010 | United States Geological Survey |
High-resolution remote sensing image | Jpeg | 2010 | Google Earth satellite imagery |
ASTER GDEM | Img | 2009 | Land Processes Distributed Active Archive Center |
Global soil classification standard data | Grid | 2009 | WestDC China |
Land use data | Esri shapefile | 2010 | The second national land survey in Guangzhou |
Urban runoff plots | Esri shapefile | 2009 | Study results of Li et al. [74] |
Impervious Surface Density | 0–0.1 | 0.1–0.2 | 0.2–0.3 | 0.3–0.4 | 0.4–0.5 | 0.5–0.6 | 0.6–0.7 | 0.7–0.8 | 0.8–0.9 | 0.9–1 |
Type Encoding | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
The Type of Impervious Surfaces | extremely low density | medium-low density | medium density | medium-high density | extremely high density |
Landscape Pattern Index | Unit | Value Range | |
---|---|---|---|
Measure of area | Percent of landscape (PLAND) | % | 0–100 |
Number of patches (NP) | pcs | >0 | |
Patch density (PD) | pcs/100 hm2 | >0 | |
Measure of shape | Mean shape index (SHAPE_MN) | no | ≥1 |
Mean related circumscribing circle (CIRCLE_MN) | no | 0–1 | |
Mean contiguity index (CONTIG_MN) | no | 0–1 | |
Measure of aggregation | Aggregation index (AI) | % | 0–100 |
Splitting index (SPLIT) | no | ≥1 | |
Patch cohesion index (COHESION) | no | ≥0 |
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Yu, H.; Zhao, Y.; Fu, Y. Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China. Int. J. Environ. Res. Public Health 2019, 16, 3613. https://doi.org/10.3390/ijerph16193613
Yu H, Zhao Y, Fu Y. Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China. International Journal of Environmental Research and Public Health. 2019; 16(19):3613. https://doi.org/10.3390/ijerph16193613
Chicago/Turabian StyleYu, Huafei, Yaolong Zhao, and Yingchun Fu. 2019. "Optimization of Impervious Surface Space Layout for Prevention of Urban Rainstorm Waterlogging: A Case Study of Guangzhou, China" International Journal of Environmental Research and Public Health 16, no. 19: 3613. https://doi.org/10.3390/ijerph16193613