River flow forecasting method

A forecasting method and flow forecasting technology, applied in the field of water resources management, can solve problems such as selecting wavelet bases and determining decomposition scales, and achieve the effect of improving accuracy and strong nonlinear fitting ability

Inactive Publication Date: 2018-10-12
WUHAN UNIV OF TECH
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Problems solved by technology

The focus of neural network in river flow prediction is mainly the determination of input variables and the optimization of the model. For example, Sivapragasam’s research on the Mississippi River used factors such as rainfall, evaporation, temperature, and flow as input variables; Yaseen et al. took the previous flow as input In the Johor River Basin in Malaysia, a comparative study of the feedforward backpropa

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[0023] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0024] A method for predicting river flow of the present invention, such as figure 1 As shown, it includes the following steps:

[0025] Step 1: Data preprocessing. The historical water level data and historical flow data of the river area to be predicted are randomly combined, and used as input conditions to perform BP neural network training and prediction, and determine a set of inputs with the smallest prediction relative error Data {u 1 (t),u 2 (t)...u n (t)} and output data o(t), where u n Represents the n-th input variable, t represents the t-th data;

[0026] Step 2: VMD model decomposition, using VMD model to input data {u 1 (t),u 2 (t)...u n (t)} and output data o(t) are respectively smoothed and decomposed into input data {u 1 (t),u 2 (t)...u n (t)}Multiple components of output data o(t), which are decomposed by VMD model to find input data{u...

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Abstract

The invention relates to a river flow forecasting method, comprising the following steps: 1: data preprocessing; 2: VMD model decomposition; 3: component reconstruction of VMD decomposition results; 4: integration of a VMD-BP model and river flow forecasting. Variational mode decomposition is an effective method to process non-stationary signals. A river flow forecasting method based on a VMD-BP model is proposed and established in combination with the advantages of a BP neural network in processing nonlinear function fitting. The method decomposes original data into a plurality of intrinsic mode functions reflecting data characteristics, and smooths the data, thereby solving the problems of nonlinearity and undulation of water level flow data, and improving the forecasting accuracy.

Description

technical field [0001] The invention relates to the technical field of water resource management, in particular to a method for predicting river flow. Background technique [0002] River flow is an important indicator of hydrological monitoring and water resources management, and it has important guiding significance and application value for water conservancy project construction, flow calculation, and shipping planning. In the benefit-cost analysis of hydraulic engineering, the combination of hydrology, engineering and economics has become a common phenomenon. Many activities related to the planning and operation of the components of water resources systems require the prediction of future events. Reasonable flow forecasting is helpful for flood forecasting and management, reservoir storage management, farmland irrigation, etc., and will bring positive economic and social benefits. At the same time, the flow measurement technology is relatively complicated, the measureme...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06N3/084G06Q10/04G06Q50/06
Inventor 黄解军赵力学李红星詹云军崔巍
Owner WUHAN UNIV OF TECH
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