Flood early warning analysis method and system based on Poisson regression and spatial filtering values

Abstract

The invention provides a flood early warning analysis method and system based on Poisson regression and spatial filtering values. The method comprises the steps that frequency statistics of flood early warning events is carried out on historical observation data of hydrological stations in a certain drainage basin; selecting disaster inducing factors and disaster forming factor data in the drainage basin, constructing a Thiessen polygon for hydrological station point coordinate points to obtain a space, and performing eigenvalue and eigenvector decomposition; under the condition that the spatial autocorrelation significance test is met, carrying out forward selective regression, and establishing a Poisson regression model based on a spatial filtering value; and adding the screened featurevectors serving as independent variables and disaster-causing and pregnancy factors into a Poisson regression model, constructing a spatial filtering value-based flood warning event frequency Poissonregression model, and supporting to realize fitting prediction of the flood warning event frequency. The method can overcome the defect of low fitting precision in the prior art, and is used for riskprediction of flood dike burst and dam break of all stations in a whole basin.

Description

technical field [0001] The invention belongs to the category of hydrological statistical analysis and spatial statistical analysis, and uses Poisson regression and spatial filtering method to analyze and realize the flood warning of hydrological stations. Background technique [0002] The dam break caused by flood has caused great loss of people's life and property in history, and is one of the common natural disasters. The risk assessment of flood disasters is mainly based on statistical methods. Qualitative methods mainly use expert opinion or data-driven methods to weight each factor or criterion, such as multi-criteria AHP, fuzzy AHP and supervised machine learning methods. Many scholars have successively used the spatial multi-criteria (fuzzy) analytic hierarchy process to model the flood risk and vulnerability in the Dongting Lake region (Yamei Wang et al., 2011, see Reference 1,; Yun Chen et al., 2015 , see reference 2). AHP is inherently subjective because it is a...

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Problems solved by technology

Chun (Chun et al., 2016, see background document 12) pointed out that although the spatial filtering method can effectively solve the problem of spatial autocorrelation and is applicable to different research fields, it also has the disadvantage of high algorithm complexity

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