Reservoir level prediction based on Embedding-GRU model
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Abstract:
The prediction of reservoir water level is of great significance in the daily operation and management of reservoir, the reinforcement of dam, the mitigation of flood disaster, and the protection of people's life and property safety. However, with the change of global temperature, the frequency of extreme weather increases and the uncertainty of abnormal rainfall increases, which lead to the lagging of reservoir level prediction methods relying on traditional engineering hydrology. Due to the high practicability of deep learning algorithms used in various fields, there are a few examples of domestic and foreign scholars using artificial intelligence to predict water levels. In order to make up for the shortcomings of single artificial intelligence model, some scholars also used the neural network model coupling method to study water level prediction, and a small number of scholars input a single variable to predict water level. The above research shows that it is feasible to use the coupled model for water level prediction, and the advantages of multiple models complement each other, and the prediction accuracy is improved to different degrees compared with the previous single model. Considering various practical factors, the monitoring data of water level of Siling Reservoir was taken as an example and the coupling prediction model of water level of reservoir was put forward based on Embedding GRU on the condition that there was only a single characteristic rainfall, in order to provide a reference for realizing the high-precision prediction of water level with single characteristics. According to the rainfall scale of the data set and the largest rainfall in the history of Zhejiang Province, the training parameter rainfall scale sets of the Embedding stage is defined as {500,550,600,650,700,750} with the accuracy of mm×10?1. In order to study the optimal parameter setting, the range of feature dimension setting was extended to {2,3,4,5,6} on the premise of adopting the control variates. The ERMS indicator was selected for this experiment. To further validate the predictive performance and generalization ability of the Embedding GRU model, an experiment was conducted based on the total daily rainfall to predict the next day's reservoir water level. The comparison algorithm is still LSTM, GRU, and BiGRU, with a total of 1826 sets of data with a data volume of 5 years.Compared with other existing artificial intelligence models of reservoir water level, the prediction accuracy is higher and the scope of reservoir is wider. In the comparative experiment of predicting the next hour's water level, the prediction ability of the four models was excellent, and they could fit the real water level data relatively accurately, which shows that the method of predicting the reservoir water level by deep learning algorithm is effective and feasible. By comparing of prediction accuracy of four models, the experiment proved that GRU algorithm is better than LSTM in prediction effect, and the embedding method can further effectively reduce the prediction error and improve the prediction accuracy of the model.It is the Embedding method that enlarges the features between rainfall and climate, coupled with lightweight deep learning algorithm GRU to predict reservoir water level. Conclusions are as follows:(1)The prediction accuracy of the Embedding GRU model is obviously better than that of LSTM, GRU, BiGRU and other single deep learning models.(2)Embedding parameters in the Embedding GRU model shall be determined by comparative test according to the actual data set.(3)The Embedding-GRU model has excellent performance in predicting different period of multiple times within 7 days, and has good prediction effect and generalization ability, which fully proves the effectiveness of the model.
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