摘要
针对水位时间序列具有线性与非线性混合、不确定性高等特点带来的预测困难问题,提出了一种基于经验模态分解(EMD)、长短时记忆网络(LSTM)和深度极限学习机(DELM)的EMD-DELM-LSTM组合模型,其中DELM和LSTM采用并联结构预测,并与EMD串联连接.首先使用EMD将原始信号分解为若干个具有单一特征的本征模态函数(IMFs),再将IMFs分类重组为高、中、低频信号后输入DELM-LSTM并联结构中进行预测并重构.以广州某大学重要湖泊为例验证模型的有效性,结果表明,与EMD-LSTM、EMD-DELM、LSTM、DELM和BiLSTM模型相比,本模型在不同时间尺度下的预测性能均有显著提升,其中40 min时间尺度下的预测性能提升效果最为明显,分别较对比模型提升43.08％、22.92％、45.79％、30.92％和47.31％.可见,本模型对于不同时间尺度的水位预测具有良好的可靠性和稳定性.
Abstract
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.
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