Flood flow prediction method and device based on deep learning and electronic equipment

Abstract

The invention discloses a flood flow prediction method and device based on deep learning, and electronic equipment. The method comprises the steps of obtaining original rainfall data, original flow data and original position information of a hydrological station; preprocessing the original rainfall data, the prior rainfall data, the original flow data and the original position information to obtain processed rainfall data, processed flow data and processed position information; forming gridding rainfall data based on the processed rainfall data, the processed prior rainfall data and the processed position information; extracting spatial distribution characteristics of the gridding rainfall data, and extracting time sequence characteristics of the rainfall data in historical T hours and future P hours to obtain a first output characteristic; for the processed flow data, extracting a time sequence characteristic of the flow data in historical T hours to obtain a second output characteristic; and performing merging classification prediction on the first output feature and the second output feature to obtain a flow prediction value of the target hydrological station in the future P hours.

Description

technical field [0001] The invention belongs to the field of flood flow forecasting, and in particular relates to a flood flow forecasting method, device and electronic equipment based on deep learning. Background technique [0002] Floods are one of the common natural disasters. Hundreds of millions of people are affected by floods and displaced every year, and the financial and material losses caused by floods are also huge. Effectively predicting flood flow and issuing early warnings is of great significance for flood control and disaster reduction. [0003] The current flood flow forecasting models are mainly divided into traditional physical models and intelligent flood forecasting models. Traditional physical models, such as the Xin'anjiang model, calculate the parameters of the physical process on the premise of fully mining the physical characteristics of the local topography, evaporation, and vegetation coverage, and finally formulate a set of regionally specific p...

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Problems solved by technology

[0004] However, most of the existing flood flow forecasting models belong to single-point forecasting, that is, they can only predict the flow situation at one time point in the future, and the flow data obtained at a single time point in the forecast lack practical application value.

Moreover, the existing flood discharge forecasting models use rainfall data only as a time series for analysis, without considering the spatial distribution of rainfall, so they cannot fully mine the information described by the actual rainfall data, and the accuracy of the prediction not tall

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