WOA-LSTM-MC-based hydrological time series prediction optimization method
A hydrological time series, forecast optimization technology, applied in forecasting, neural learning methods, data processing applications, etc., can solve the problems of small sample set model accuracy, time-consuming training, large errors, etc., to ensure global search capabilities and local The effect of exploring ability and speeding up the convergence speed
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[0067]Example 1: This example is predicted on the Hankou Hydrology Station of the Yangtze River Basin, such asFigure 5 withFigure 6 As shown, including the following steps:
[0068]Step S1, the data set of this embodiment selects the hourly traffic data of Hankou Hydrology, January 1, 2019, from January 1, 2020, of which 8:00 to 2020, January 2019 5 At 7:00 on the 15th, 12,000 data were taken as a training set, and from 8:00 on June 12, 2020, 4351 data were 4351 data as a test set.
[0069]Step S11, the present embodiment sets the traffic data of the Hankou Hydrology as a forecasting factor, and sets a 1-24-hour foreseeable period, and different predictive models are established for different foreseevelopments. By correlation analysis, the first T is selected1Hourly prediction T2Hours, T1Get through correlation analysis, T2For the foresee period.
[0070]Step S21, the present embodiment takes the flow data of Hankou Station in the Yangtze River Basin as an example, and the adaptivity value i...
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