Multi-time Scale Prediction for Lake Water Level Based on EMD-DELM-LSTM Combined Model
Given the challenges associated with predicting water level time series,attributed to their mixed linear and nonlinear characteristics and high uncertainty,we propose a combined model,termed EMD-DELM-LSTM,in-tegrating empirical mode decomposition(EMD),long-short-term memory network(LSTM),and deep extreme learning machine(DELM).In this framework,DELM and LSTM operate in parallel and in series with EMD.Ini-tially,the original signal is decomposed into distinct intrinsic mode functions(IMFs)via EMD,categorizing them into high,medium,and low frequency signals.These signals are then fed into the DELM-LSTM parallel structure for prediction and reconstruction.To validate the efficacy of the model,we utilize data from a lake at a university in Guangzhou.Results indicate superior performance compared to EMD-LSTM,EMD-DELM,LSTM,DELM,and BiLSTM models across various time scales,with the most pronounced enhancement observed at the 40-minute scale.Notably,performance improves by 43.08％,22.92％,45.79％,30.92％,and 47.31％when compared to the re-spective reference models.These findings underscore the reliability and stability of our proposed model for water lev-el prediction across different temporal scales.
water level predictionEMD-DELM-LSTMempirical mode decompositionmulti-time scale analysisartificial neural network
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