Dredging channel back-silting amount predicting method based on timing sequence analysis-Markov chain method

Abstract

The invention belongs to the field of hydraulic engineering, and particularly relates to a dredging channel back-silting amount predicting method based on a timing sequence analysis-Markov chain method. The method includes the following steps that an original sequence of spatial dispersion is selected; a predicted value sequence is generated; the relative residual sequence of the original sequence and the predicted value sequence is calculated; the relative residual sequence is divided into intervals; Markov chain prediction is performed among the intervals; the water depth prediction interval is averaged to obtain a predicted water depth value, and an original water depth value is substracted from the water depth value to obtain a back-silting value. A timing sequence analysis method and the Markov chain are combined to obtain the timing sequence analysis-Markov chain prediction method, the change trends and random fluctuation of the sequences can be reflected at the same time, and the predicting method conforms to the actual change conditions of the dredging channel back-silting sequence better. By using the predicting method, the relative error rate is only 1.04%, the timing sequence analysis-Markov chain method can improve the precision by 97% compared with a single timing sequence method, and the prediction of the timing sequence analysis-Markov chain method conforms to actual measurement data better compared with the grey theory.

Description

technical field [0001] The invention relates to a method for predicting the amount of silting in a dredging tank, which belongs to the field of water conservancy engineering, and in particular to a method for predicting the amount of silting in a dredging tank by using a time series analysis-markov chain prediction method. Background technique [0002] The silting prediction of dredging tanks is an important basis for the development of port dredging projects. Due to problems such as the inability to arrange long-term observation stations in navigable waters, the high cost of bathymetry of harbor basins and channel dredging tanks, the lack of data, and the short sequence of measured data, the dredging tanks are silted back. The amount of data available for prediction is small, and it is necessary to predict the backsilting volume of dredged tanks on the basis of short sequences. [0003] Existing time series forecasting method and gray theory model silting amount forecasting...

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Problems solved by technology

[0003] Existing time series forecasting method and gray theory model silting amount forecasting method, but because the time series ARMA model is more suitable for predicting long series, and the terrain measurement in the dredged tank is more difficult, so the length of the data series is not enough, so the simulation situation is poor, the result The overall size is too small, resulting in large-scale erosion of the dredged tank as a whole

The gray theory is suitable for predicting the sequence of monotonous growth. The amount of sedimentation in the dredging tank is determined by the amount of sediment deposition, which is affected by a series of factors such as water flow and sediment content. It has a certain degree of randomness, so the simulation situation does not match the actual situation.

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