Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Sources
3. Methodology
3.1. Regression Model
3.2. Data Processing
3.2.1. Waterlogging Risk Areas
3.2.2. Waterlogging Spot Density
3.2.3. The Relationships of Waterlogging Spots
3.2.4. Spatial Autocorrelation of Waterlogging Spots
3.2.5. Attribute Extraction
3.3. Explanatory Variables
3.4. Integrated Frameworks
4. Results and Discussion
4.1. Waterlogging Frequency and Spatial Distribution Characteristics of Waterlogging Spots
4.2. OLS Analysis
4.2.1. Finding Key Explanatory Variables
4.2.2. Checking Relevant Parameters of OLS Model
4.3. GWR Analysis
4.3.1. Performance of GWR Model
4.3.2. Regression Coefficient of GWR Model
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data | Format | Time | Source |
---|---|---|---|
Waterlogging information | Text | 2012–2018 | Google [89] |
Landsat satellite data | Raster | 2018-6-07 | USGS [90] |
Topographic data | Raster | 2011-10-17 | USGS [91] |
High-resolution remote sensing image | Raster | 2018 | Google Earth Pro |
Administrative areas | Shapefile | 2018 | DIVA-GIS [92] |
Traffic map data | Shapefile | 2018 | OpenStreetMap [93] |
Population data | Text | 2013–2017 | Hanoi Portal [94] Hanoi Urban Planning Institute [95] |
Model | Explanatory Variables | Coefficient | Coefficient Statistical Significance (p-Value) | Adjusted-R2 |
---|---|---|---|---|
Model 1 | POP-Dens | 2.094 × 100 | p < 0.01 * | 0.659411 |
Road-Dens | 1.931 × 104 | p < 0.01 * | ||
NDVI | −8.936 × 105 | p < 0.01 * | ||
DW-Dist | 6.941 × 101 | p < 0.01 * | ||
DEM | −3.371 × 103 | p > 0.01 | ||
ISP | 1.589 × 105 | p < 0.01 * | ||
Model 2 | POP-Dens | 1.514 × 100 | p < 0.01 * | 0.654604 |
Road-Dens | 2.067 × 104 | p < 0.01 * | ||
NDVI | −8.742 × 105 | p < 0.01 * | ||
DW-Dist | 6.546 × 101 | p < 0.01 * | ||
ISP | 1.451 × 105 | p < 0.01 * |
Explanatory Variables | Coefficient | Probability | Robust_Pr | VIF | Adjusted-R2 | AICc | BP |
---|---|---|---|---|---|---|---|
Intercept | −3.411 × 105 | p < 0.01 * | p < 0.01 * | - | 0.654604 | 3598.435 | p < 0.01 * |
POP-Dens | 1.514 × 100 | p < 0.01 * | p < 0.01 * | 2.61 | |||
Road-Dens | 2.067 × 104 | p < 0.01 * | p < 0.01 * | 2.21 | |||
NDVI | −8.742 × 105 | p < 0.01 * | p < 0.01 * | 1.83 | |||
DW-Dist | 6.546 × 101 | p < 0.01 * | p < 0.01 * | 1.34 | |||
ISP | 1.451 × 105 | p < 0.01 * | p < 0.01 * | 3.11 |
Research Period | Spatial Autocorrelation Report | GWR Model | |||
---|---|---|---|---|---|
Adjusted-R2 | AICc | ||||
2012–2018 | Moran’s I = 0.006 | z = 0.844 | p = 0.398 | 0.676 | 3616.753 |
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Tran, D.; Xu, D.; Dang, V.; Alwah, A.A.Q. Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources. Water 2020, 12, 879. https://doi.org/10.3390/w12030879
Tran D, Xu D, Dang V, Alwah AAQ. Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources. Water. 2020; 12(3):879. https://doi.org/10.3390/w12030879
Chicago/Turabian StyleTran, Ducthien, Dawei Xu, Vanha Dang, and Abdulfattah.A.Q. Alwah. 2020. "Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources" Water 12, no. 3: 879. https://doi.org/10.3390/w12030879