Data-driven small watershed real-time flood forecast method

Abstract

The invention discloses a data-driven small watershed real-time flood forecast method comprising the following steps: (1) constructing a delay time sequence and a mean time sequence based on rainfall and river flow data; (2) calculating the grey relevance between the time sequences, and selecting the higher-correlation time sequence as modeling sample data; (3) modeling input-output data by use of a machine learning algorithm to get forecast models and a fitting value; (4) calculating the weight of each forecast model based on the information entropy theory; and (5) forecasting the river flow by use of the forecasting models, and performing weighted calculation on single-model forecast results to get a forecasted value of river flow. According to the invention, the complex nonlinear mapping relationship of small watershed flood is simulated by making full use of rainfall and river flow data, the information in the data is mined, and therefore, the accuracy of real-time small watershed flood forecast is improved.

Description

technical field [0001] The invention relates to a flood forecasting method, in particular to a data-driven real-time flood forecasting method for small watersheds. Background technique [0002] Small watersheds have high mountains and steep slopes, dense streams, steep rises and falls in water levels, and fierce onslaughts, often causing disasters in a short period of time, and may cause secondary disasters such as landslides, landslides, avalanches, and mud-rock flows, causing huge losses of life and property of people along the river. Therefore, forecasting techniques for small watershed floods are of great value. At present, most flood forecasting models belong to the category of deterministic hydrological models, and can be further divided into three categories: conceptual models, physical models and empirical models. The conceptual model, also known as the "grey box" model, divides the flow-confluence process of the watershed into multiple calculation units, and descri...

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Problems solved by technology

Due to the above characteristics, it is difficult to directly calculate the parameters of conceptual models and physical models from the physical properties of small watersheds, and it is impossible to accurately obtain the boundary conditions and initial conditions of small watershed floods, resulting in high computational complexity and low accuracy of flood forecasting

The traditional empirical model is difficult to deal with the nonlinear relationship between rainfall and runoff in small watershed floods, resulting in long calculation time and unstable calculation results

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