基于EMD-DELM-LSTM组合模型的湖泊水位多时间尺度预测

Multi-time Scale Prediction for Lake Water Level Based on EMD-DELM-LSTM Combined Model

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。

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,integrating 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.Initially,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 respective reference models.These findings underscore the reliability and stability of our proposed model for water level prediction across different temporal scales.