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Open AccessArticle

Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression

School of Resource and Environment Science, Wuhan University, Wuhan 430079, China
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Water 2019, 11(10), 1969; https://doi.org/10.3390/w11101969
Received: 20 August 2019 / Revised: 12 September 2019 / Accepted: 17 September 2019 / Published: 21 September 2019
(This article belongs to the Special Issue Flood Risk Assessments: Applications and Uncertainties)
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. View Full-Text
Keywords: spatial autocorrelation; Poisson regression; eigenvector spatial filtering method; flood risk evaluation spatial autocorrelation; Poisson regression; eigenvector spatial filtering method; flood risk evaluation
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Fang, T.; Chen, Y.; Tan, H.; Cao, J.; Liao, J.; Huang, L. Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression. Water 2019, 11, 1969.

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