Multi-model Integrated Flood Forecasting Method Based on Propagation Time Cluster Analysis

Abstract

The invention discloses a multi-model integrated flood forecasting method based on propagation time clustering analysis and belongs to the technical field of hydrologic forecasting. The method includes adopting a derived dynamic time warp matching method for floor process similarity analysis; estimating flow propagation time of each station in the upstream and the downstream; decomposing a sample into a plurality of clusters by performing clustering analysis on the flow propagation time; respectively building an SVM regression model for each sub-flow sequence to simulate the flood forming process; combining the sub-models into a comprehensive model. Comparing a comprehensive forecasting result acquired by the method with a model forecasting result acquired by a single model under conventional conditions and on the basis of flow clustering, the comparison result shows that the comprehensive model is better in comprehensive performance.

Description

technical field [0001] The invention relates to a flood forecasting method, in particular to a multi-model integrated flood forecasting method based on propagation time cluster analysis, and belongs to the technical field of hydrological forecasting. Background technique [0002] In hydrological time series forecasting and analysis, runoff forecasting is a classic hydrological problem, which is of great significance to water resource allocation management and flood control and disaster reduction scheduling decisions. However, due to the comprehensive influence of upstream site flow, climate and underlying surface and other factors, the flow often shows complex nonlinear and non-stationary characteristics. [0003] At present, there are many methods for hydrological time series forecasting, among which the simplest is the linear model forecasting method, such as AR, ARMA and so on. However, it is difficult to achieve good results by using linear models to predict nonlinear t...

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Problems solved by technology

[0004] Different flow rates and different water flow velocities lead to changes in the propagation time of floods, and large changes in the amount of rainfall in different time periods, which eventually lead to different emphases in the input-output relationship of rainfall runoff forecasting. If only a single model is used, Difficult to fit all parts of the data well at the same time

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