基于经验小波变换的鄱阳湖CODMn预测

高锰酸盐指数(COD_Mn)是衡量水质状况的最重要参数之一，能反映水体受还原性物质污染的程度。结合经验小波变换(EWT)和双向长短期记忆(BLSTM)神经网络，提出了一种先利用EWT将原始的COD_Mn时间序列分解成若干成分，然后利用BLSTM神经网络对分解出来的每个成分进行预测，最后将所有成分的预测结果重建获得最终COD_Mn预测值的新的混合模型EWT-BLSTM；并以2017年8月—2020年4月鄱阳湖COD_Mn监测数据为研究对象，进行模型性能验证。结果表明：EWT-BLSTM模型具有良好的预测性能，预测未来1 d以后的COD_Mn时，EWT-BLSTM模型的平均绝对百分比误差为2.25%，与单一BLSTM神经网络模型相比降低了10.53%；预测未来7 d以后的COD_Mn时，EWT-BLSTM模型的平均绝对百分比误差为8.36%，与单一BLSTM神经网络模型相比降低了16.16%。在COD_Mn峰值处，该模型依然保持较高稳定的预测性能，说明在数据相对复杂、极端的情况下，该模型依然适用。

Forecasting CODMn of Poyang Lake based on empirical wavelet transform

Permanganate index (CODMn) is one of the most important parameters to measure water quality and could reflect the degree of water pollution by reducing substances. A novel CODMn forecast model (EWT-BLSTM) by combining empirical wavelet transform (EWT) and bidirectional long short-term memory (BLSTM) neural network was proposed. First, the original CODMn time series was decomposed into several components by EWT. Next, BLSTM neural network was employed to predict each decomposed component. Finally, the predictions of all components were reconstructed to obtain the new hybrid model EWT-BLSTM for the final CODMn predictions. CODMn data of Poyang Lake was used to evaluate the proposed forecast model. The results showed that EWT-BLSTM model had a powerful forecast capacity. For 1, 7-day ahead forecasting, the mean absolute percentage error (MAPE) of the forecast by EWT-BLSTM was 2.25% and 8.36%, respectively. The MAPE reduced by EWT-BLSTM over BLSTM was 10.53% for 1-day ahead forecasting and 16.16% for 7-day ahead forecasting. Furthermore, the proposed model showed highly stable forecasting performance for CODMn peak points, indicating that the proposed method was still applicable in the case of relatively complex data with extreme points.