Monthly runoff grey prediction method and device, computer equipment and storage medium

Abstract

The invention relates to a monthly runoff grey prediction method and device, computer equipment and a storage medium. The method comprises the steps of obtaining monthly runoff original data; carryingout secondary smoothing processing on the monthly runoff original data to obtain processed monthly runoff data; performing data modeling processing according to the monthly runoff data to obtain a monthly runoff grey forecasting equation; and performing monthly runoff data prediction according to the monthly runoff grey prediction equation to obtain monthly runoff prediction data. Modeling processing is carried out on monthly runoff data after secondary smoothing processing, a more accurate monthly runoff grey prediction equation can be established, monthly runoff prediction data with small errors are obtained, and the prediction precision of the simulated monthly runoff prediction data is guaranteed.

Description

technical field [0001] The present application relates to the technical field of monthly runoff forecasting, in particular to a method, device, computer equipment and storage medium for gray forecasting of monthly runoff based on secondary smoothing. Background technique [0002] At present, in hydrological fields such as hydropower stations, reservoirs, rivers and lakes, etc., with the development of related technologies in hydrological nonlinear time series analysis such as rainfall, groundwater depth, and annual runoff forecasts, results have gradually been achieved. Significantly, at present, most of the single data processing methods are adopted before monthly runoff data forecasting and modeling. [0003] In the traditional monthly runoff data forecasting method, the initial data is smoothed. However, due to the sudden change and extreme unsmoothness of the monthly runoff data, there are uncertainties, resulting in errors in the forecasted monthly runoff data, which do...

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Problems solved by technology

[0002] At present, in hydrological fields such as hydropower stations, reservoirs, rivers and lakes, etc., with the development of related technologies in hydrological nonlinear time series analysis such as rainfall, groundwater depth, and annual runoff forecasts, results have gradually been achieved. It is of great significance. At present, most of the monthly runoff data forecasting and modeling are single data processing methods.

[0003] In the traditional monthly runoff data forecasting method, the initial data is smoothed. However, due to the sudden change and extreme unsmoothness of the monthly runoff data, there are uncertainties, resulting in errors in the forecasted monthly runoff data, which do not meet the hydrological forecast specifications. request, unable to get application

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