Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources
| Autor: | Abdulfattah.A.Q. Alwah, Vanha Dang, Dawei Xu, Ducthien Tran |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Hydraulic engineering
spatial analysis 010504 meteorology & atmospheric sciences Geography Planning and Development regression models Climate change Context (language use) 010501 environmental sciences Aquatic Science 01 natural sciences Biochemistry Normalized Difference Vegetation Index internet open-data sources lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Urban planning Urbanization Impervious surface 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 Regression analysis Geography urban waterlogging risk Water resource management ArcGIS Waterlogging (agriculture) |
| Zdroj: | Water Volume 12 Issue 3 Water, Vol 12, Iss 3, p 879 (2020) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w12030879 |
| Popis: | In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012&ndash 2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging. |
| Databáze: | OpenAIRE |
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