Method for forecasting flood based on Boosting algorithm and support vector machine

Abstract

The invention discloses a method for forecasting flood based on a Boosting algorithm and a support vector machine, which comprises following steps: use the correlation coefficient method to determine the forecast factors; utilize kernel principal component analysis to process the forecast factors with dimension reduction; utilize the Boosting algorithm to select a sample and establish a plurality of support vector machine prediction models, introduce loss function and the correlation coefficient to adjust sample weight, and finally combine the plurality of prediction models as a total prediction model; and utilize the total prediction model to predict a test sample. In the invention, the previous steps are about data pre-processing, which aims to extract useful information in flood datum and eliminate disturbance of redundant information to the forecast; in the third step, the Boosting algorithm is introduced into the flood forecast so as to try to extract a sample of one model that can't learn well for training the next model; in this way, the accuracy of flood forecast can be improved effectively by the combined model; and the last step is used for testing the model effect. According to the experimental datum, the forecast accuracy can be improved effectively by the technical solution.

Description

technical field [0001] The invention relates to flood forecasting technology, in particular to a flood forecasting method based on Boosting algorithm and support vector machine. Background technique [0002] Flood forecasting is the most important non-engineering project for disaster prevention and reduction. It has played an important role in flood control and drought relief, water resource management and protection, and water project operation management over the years, and has achieved remarkable economic and social benefits. However, the flood process is affected by many factors such as the natural geography of the basin, hydrology, meteorology, human activities, etc., and is highly complex and uncertain. Therefore, it is a difficult problem to be solved urgently to accurately forecast the water and rain conditions in order to generate dispatching schemes. [0003] Existing flood forecasting methods are mainly divided into two categories: one is based on process-driven ...

AI Technical Summary

Problems solved by technology

Therefore, there are problems such as excessive dependence on the quality and quantity of training data, complex model structure, and easy to fall into local extremum.

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