Flood forecasting method suitable for runoff data lack drainage basin based on machine learning

Abstract

The invention discloses a flood forecasting method suitable for a runoff data lack basin based on machine learning. The flood forecasting method comprises the following steps: 1) extracting and parameterizing sample basin features; 2) carrying out basin flood response characteristic analysis; 3) generating a drainage basin feature sample set; 4) generating a classification tree based on the basinfeature sample set; 5) generating a training data set based on the tree nodes; 6) carrying out flood forecasting based on the classification tree and the data driving model; and 7) updating the classification tree and the training set. Flood response characteristics of the drainage basin are analyzed by utilizing a machine learning algorithm; and based on the watershed characteristics and the flood response characteristics, an association relationship between watersheds is established. According to the method, the sample data set is generated on the basis of the basin characteristics and the flood response similarity, then the data driving model is trained according to the sample data set, the rainfall and flood response relation of the medium and small rivers is simulated, and therefore real-time forecasting of the flood of the medium and small rivers is achieved. According to the method provided by the invention, the data driving model can be applied to flood forecasting of runoff data lack drainage basins, and the dependence of a previous parameter transplanting mode on a model structure and model parameters is changed, so that the flood forecasting precision is improved.

Description

technical field [0001] The invention belongs to the technical field of water conservancy engineering, in particular to the technical field of flood control forecasting, and specifically relates to a machine learning-based flood forecasting method suitable for watersheds lacking runoff data. Background technique [0002] At present, my country's major rivers and their main tributaries have formed a flood control engineering system based on dikes, reservoirs, and flood storage and detention areas. Non-engineering measures such as flood control early warning and forecasting systems have also been gradually strengthened, which can basically prevent major rivers from flooding. However, for more than 50,000 small and medium-sized rivers, their distribution is wide, their number is large, their natural geography and climatic conditions are complex and diverse, and their flood control capabilities are generally backward. Especially in recent years, extreme weather events have increas...

AI Technical Summary

Problems solved by technology

However, data-driven models often require a large amount of rainfall and runoff data to train the model, so they have not been applied to flood forecasting in basins where runoff data is scarce.

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