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

A technology of early warning analysis and space, applied in the fields of instrumentation, calculation, electrical and digital data processing, etc., can solve the problem of high algorithm complexity, and achieve the effect of improving the accuracy of fitting and prediction

Active Publication Date: 2019-07-16
WUHAN UNIV
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Chun (Chun et al., 2016, see background document 12) pointed out that although the spatial filtering method can effectively solve the pro

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

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[0065] In order to facilitate the understanding, implementation and application of this invention by those skilled in the art, the invention will be further explained below with reference to the accompanying drawings and examples. Not just limited to this invention.

[0066] The embodiment of the present invention adopts a quantitative method based on supervised learning, which can find out the main driving factors that trigger flood warning events in the basin, and can be used to predict the expected frequency of warning events on the entire river, thereby providing decision-making for the location of hydrological stations support.

[0067] In the embodiment, a flood warning analysis method based on Poisson regression and spatial filtering value, the flow is shown in the appendix. figure 1 , perform the following steps:

[0068] Step 1: Count the frequency of flood warning events on the historical observation data (including time, water level, flow, warning water level, stati...

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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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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20Y02A10/40
Inventor 陈玉敏方涛李慧芳谭黄元曹吉平罗凤兰
Owner WUHAN UNIV
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