Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression
| Autor: | Yumin Chen, Huangyuan Tan, Fang Tao, Huang Liheng, Liao Jiaxin, Jiping Cao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
lcsh:Hydraulic engineering
010504 meteorology & atmospheric sciences flood risk evaluation Geography Planning and Development 010501 environmental sciences Aquatic Science 01 natural sciences Biochemistry spatial autocorrelation Standard deviation eigenvector spatial filtering method Poisson regression symbols.namesake lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Statistics Linear regression Spatial analysis 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 Flood myth Elevation Regression analysis Stepwise regression symbols Environmental science |
| Zdroj: | Water Volume 11 Issue 10 Water, Vol 11, Iss 10, p 1969 (2019) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w11101969 |
| Popis: | A Poisson regression based on eigenvector spatial filtering (ESF) is proposed to evaluate the flood risk in the middle reaches of the Yangtze River in China. Regression analysis is employed to model the relationship between the frequency of flood alarming events observed by hydrological stations and hazard-causing factors from 2005 to 2012. Eight factors, including elevation (ELE), slope (SLO), elevation standard deviation (ESD), river density (DEN), distance to mainstream (DIST), NDVI, annual mean rainfall (RAIN), mean annual maximum of three-day accumulated precipitation (ACC) and frequency of extreme rainfall (EXE) are selected and integrated into a GIS environment for the identification of flood-prone basins. ESF-based Poisson regression (ESFPS) can filter out the spatial autocorrelation. The methodology includes construction of a spatial weight matrix, testing of spatial autocorrelation, decomposition of eigenvectors, stepwise selection of eigenvectors and calculation of regression coefficients. Compared with the pseudo R squared obtained by PS (0.56), ESFPS exhibits better fitness with a value of 0.78, which increases by approximately 39.3%. ESFPS identifies six significant factors including ELE, DEN, EXE, DIST, ACC and NDVI, in which ACC and NDVI are the first two main factors. The method can provide decision support for flood risk relief and hydrologic station planning. |
| Databáze: | OpenAIRE |
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