Research On Water Level Prediction Model Of Luoma Lake Based On Long-short-term Memory Recurrent Networks And Its Variants

Water level forecasting is an important part in hydrological forecast,and it plays a key role in water dispatching.Due to the influence of climate,human activities,underlaying surface etc,water level time series has great uncertainty.Therefore,promptly and accurately water level forecasting is significance for Reservoir Scheduling and water resources allocation.Taking Luoma Lake as the research object,this paper studies the influence of pre monthly water level on monthly water level prediction,and the achievements are as follows:(1)Four different factor optimization schemes were established.Copula function,grey relation analysis(GRA)and empirical mode decomposition(EMD)are used to form four factor optimization schemes,namely Copula,GRA,EMD-Copula and EMD-GRA.It can be seen from the four model evaluation indexes,the empirical mode decomposition(EMD)is better than the empirical mode decomposition(EMD)scheme,which indicates that EMD can improve the prediction accuracy of the model.(2)Long-short-term memory networks(LSTM),bidirectional long-short-term memory networks(Bi LSTM),gated-recurrent-unit(GRU)and the coupling model L-B-G were established.It can be seen from the four model evaluation indexes,coupling model is better than the single model in training and test period.The coupling model is effective as a prediction model to improve the accuracy and stability of the simulation.To be specific,in the training period,the root-mean-square error(RMSE)of EMD-GRA-L-B-G model is 0.26,and the proportion of absolute errors which are smaller than 0.1 m,0.2 m and 0.3 m respectively accounts for 70.24%,82.14% and 91.43% respectively.By contrast,in the test period,the RMSE of EMD-GRA-L-B-G model is 0.26 and the proportion of absolute errors which are smaller than 0.1 m,0.2 m and 0.3 m respectively account for 70.56%,83.33% and90.56%,reflecting the optimal prediction effect.(3)Interval prediction was also made for water level.First of all,calculate the prediction interval where the overall prediction time is below 90% level of confidence and use the corresponding indexes to quantify the prediction interval.Then divide the prediction time,and make interval prediction respectively per month so as to calculate the prediction interval under 90% level of confidence respectively per month(appropriate interval prediction).It was found through comparative analysis that the prediction quality of appropriate interval prediction is much better than that of single interval prediction and the appropriate interval prediction also stands out in terms of appropriateness.