Flood forecasting method based on cluster analysis and real time correction

Abstract

The invention discloses a flood forecasting method based on cluster analysis and real time correction, which comprises the following steps: 1) using PCA(Principal Component Analysis) to perform dimensionality reduction to the input of a model; 2) using the K-means clustering method to conduct clustering analysis on original data; dividing the flood data into different classifications; and then training different SVM models; when a testing sample is inputted, using the clustering center to determine the classification of the test sample and predicting the corresponding model to obtain a predicted value q; and 3) using a BP neural network for real time correction; calculating the error sequence between the predicated value and the actual value; using the error sequence data to train the BP neural network error correction model to obtain the error correction value qe. The final forecasting result is the model predicted value q plus the error correction value qe. According to the invention, the original hydrological data are divided into several classifications by cluster analysis, and through the training of the models, forecasting can be available by the multiple models. Then, real-time correction is achieved by the BP neural network to improve the forecasting accuracy for the time of flood peak.

Description

technical field [0001] The invention belongs to the technical field of water flow forecasting, in particular to a flood forecasting method based on cluster analysis and real-time correction. Background technique [0002] Flash floods are sudden floods caused by rainfall in small and medium-sized rivers in mountainous areas. However, there are many small and medium-sized rivers in my country, and most of the small and medium-sized rivers have a sparse network of stations. The necessary emergency monitoring methods are lacking, and the forecasting scheme is not perfect. In recent years, due to the increase in extreme weather events, local heavy rainfall and sudden torrential rain often occur, while mountainous areas have high mountains and steep slopes, short river sources and rapid streams, and mountain disasters such as mountain torrents, mudslides, and landslides are prone to occur in heavy rainy weather , resulting in the loss of people's lives and property. Therefore, th...

AI Technical Summary

Problems solved by technology

Although the combined model solves the multi-model problem contained in the historical flood data, the problem of the general deviation of the data-driven model for the forecast results of the flood peak time has not been well solved

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