Neural network method for predicting river flow rate based on multi-section water levels

Abstract

The invention relates to a neural network method for predicting river flow rate based on multi-section water levels. The neural network method includes the steps of acquiring known flow rate data and water level data of a to-be-solved hydrometric station last year as well as water level data of m upstream gauging stations and n downstream gauging stations of the hydrometric station, and numbering the water level data so as to obtain water level data of the gauging stations; enabling the water level data measured at the same time last year, the water level data of the adjacent upstream gauging stations and the water level data of the adjacent downstream gauging stations to form groups of independent variables Hi; subjecting each group of independent variables Hi and flow rate data Qi at the corresponding time to BP neural network fitting; according to a time corresponding to known to-be-solved flow rate data Qm, taking the water level data measured at the time as independent variables Hm; substituting the independent variables Hm into a BP neural network model to obtain the corresponding flow rate data Qm so as to obtain a water level-flow rate relation curve and achieve prediction on the river flow rate.

Description

technical field [0001] The invention relates to a method for predicting river flow, in particular to a neural network method for predicting river flow based on multi-section water levels. Background technique [0002] At present, the flow data of natural rivers are important data in the research process of many disciplines such as hydrology and river dynamics. However, in the past engineering experience, it is very difficult to directly measure the flow of the actual river, and it is time-consuming, costly, and has great limitations. In contrast, water level data measurement is simple and easy, with complete data. If the relationship between river discharge and water level can be obtained from the existing water level data and flow data, and the water level-flow curve can be drawn, then the flow data can be obtained from the water level data, which greatly improves the efficiency of scientific research and engineering. [0003] In the previous water level-discharge curve m...

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Problems solved by technology

[0009] With the gradual development of water energy, the water storage and discharge process of the reservoir has had an important impact on the water level-flow relationship of each hydrological station, especially the hydrological station located at the end of the reservoir. During the storage period: the water level of the reservoir rises, The water level of the section also increases correspondingly, but the flow rate is still low, and the original water level-flow curve is no longer applicable

Taking the year as the time scale, due to the impact of reservoir regulation and storage, the flow of the corresponding river section does not meet the condition of constant flow, and the hydraulic gradient J is quite different at different time periods throughout the year, resulting in a gap between water level and flow. A corresponding relationship is destroyed, and the original water level-flow curve is no longer applicable

However, the relationship between water level and flow in actual rivers is very complicated, especially in river sections affected by water conservancy and hydropower projects, often appearing in the shape of multi-socket curves, and the water level and flow of a single section of a river show obvious non-monotonicity The accuracy of single-value curve fitting is very poor, which affects the progress of scientific research and engineering

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