WOA-LSTM-MC-based hydrological time series prediction optimization method

Abstract

The invention discloses a WOA-LSTM-MC-based hydrological time series prediction optimization method. The method comprises the steps of performing parameter optimization on part of parameters of a prediction model by using an optimized whale optimization algorithm; selecting flow data of the hydrological station to be predicted as experimental data; dividing the data set into a training set and a test set, and performing training and prediction; using a Markov chain MC for correction, so that a final hydrological prediction result, namely a more accurate prediction value, is obtained; and establishing a hybrid WOA-LSTM-MC (White Object Model Management Controller) model. According to the method, the optimal parameters required by the prediction model can be found more quickly and accurately, the global search capability and the local exploration capability of the algorithm can be ensured, and the convergence rate is high; and the prediction result is accurate.

Description

Technical field[0001]The present invention belongs to hydrological prediction techniques, and more particularly to a hydrological time sequence prediction optimization method based on WOA-LSTM-MC.Background technique[0002]With the development of today's computer and software technology, its application is increasing, the massive data of time series is more visible. On the one hand, some of these data can be used for us to study, so massive time series Data mining techniques are born, and we can perform the status of future data through the law and development trends, so it is critical to the analysis and prediction of time series.[0003]Hydrological data is the discrete record, evaporation amount, rainfall, and water level, etc. of the hydrological process, and the standard time series. Hydrological data has the characteristics of large data volume, fast update, and complicated types, which are also related to many of the season, geommon, and hydrological laws. How to effectively ana...

AI Technical Summary

Problems solved by technology

The effect of hydrological time series prediction is remarkable, but there are still some problems. For example, the accuracy of the model for small sample sets will be affected to a certain extent. There are problems that the larger the amount of data, the larger the error, and the more time-consuming the training. The prediction model itself is also difficult. There are some problems, so it is crucial to choose an algorithmic model with a certain robustness

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