宽度-深度融合时频分析的径流智能预测方法

Runoff Intelligent Prediction Method Based on Broad-deep Fusion Time-frequency Analysis

为解决现有基于LSTM的径流预测模型易陷入局部最优的问题,提出了基于VMD-LSTMBLS(variational mode decomposition-LSTM-broad learning system)的径流预测模型。将宽度学习系统与LSTM结合,针对径流序列多噪音特点,采用时频分析方法中的变分模态分解,将径流时间序列的一维时域信号变换到二维时频平面,减少噪声对预测结果的影响。仿真结果表明:与基线模型及现有基于LSTM的径流预测模型相比,该模型的预测精度有较为明显的提高。

Broad learning system(BLS)is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization.To reduce the influence of noise on the prediction results,the variational mode decomposition(VMD)is adopted to transform the onedimensional time-domain runoff signal to the two-dimensional time-frequency plane.The runoff prediction model based on VMD-LSTM-BLS is proposed.The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.