基于EMD-LSTM模型的河流水量水位预测

基于经验模式分解方法和长短期记忆网络(empirical model decomposition and long short-term memory network，EMD-LSTM)模型对水位数据进行预测。先采用中值滤波对数据序列进行预处理，然后对数据序列进行EMD分解，并对EMD分解的每个特征序列使用LSTM模型进行预测，最后叠加各个序列预测值，得到最终的预测结果。以南水北调工程某河流每隔1 h的瞬时流量、流速和水深监测数据为研究对象，采用EMD-LSTM模型进行建模，试验结果表明，该模型能够实现水位、水速和瞬时流量连续12 h和6 h的准确预测，且比LSTM模型具有更高的预测精度，可为水位预判和水资源的实时调度提供决策依据。

Prediction of river water flow and water level based on EMD-LSTM model

The combination of empirical model decomposition and long short-term memory network(EMD-LSTM)model was used to forecast the hydrological time series data. The median filter for data preprocessing was used first and then EMD was applied to decompose the time sequence. The LSTM model was used to prediction for each characteristic series from EMD, and each prediction sequence was superposed to obtain the final prediction result. Based on the data of the instantaneous flow, water velocity and water level per hour of a certain river in the South-to-North Water Diversion Project, the EMD-LSTM was used for modeling. The experimental results show that this method can realize accurate prediction of water level, water velocity and instantaneous flow for 12 h and 6 h continuously, has higher accuracy compared with the LSTM model and provides decision basis for the forecast of hydrological time series and the real-time dispatching of water resources.