基于SFLA-CNN和LSTM组合模型的水位预测

Water Level Prediction Based on SFLA-CNN and LSTM Combined Model

水文时间序列受降雨量的影响,在变化规律上呈现季节性、非线性的特点。传统单一模型结构简单,对于复杂的非线性水文时间序列具有预测精度较低、不能很好捕捉水文时间序列的复合特征的问题。组合预测模型采用多分类器的思想,能够有效地提高预测准确度,然而在模型参数选择方面需要手工调参,花费时间多且不准确。本文提出一种基于SFLA-CNN和LSTM的组合预测模型:通过随机蛙跳算法SFLA对CNN模型进行参数寻优,得到优化后的SFLA-CNN预测模型;之后利用BP神经网络对SFLA-CNN和LSTM模型的预测值进行非线性组合,获得最终预测结果。在江苏省太湖区域的水位预测实验结果表明,该组合模型与现有模型相比,有效地提高了预测准确率,具有更好的泛化能力。

Hydrological time series are affected by rainfall,showing seasonal and non-linear characteristics.The traditional single model is simple in structure,which has the problems of low prediction accuracy for complex nonlinear hydrological time series and can not capture the composite characteristics of hydrological time series well.The combined forecasting model adopts the idea of multi-classifier,which can effectively improve the forecasting accuracy.However,it is time-consuming and inaccurate to manually adjust the model parameters.In this paper,a combined forecasting model based on SFLA-CNN and LSTM is proposed:The parameters of CNN model are optimized by Shuffled Frog Leaping Algorithm,and the optimized SFLA-CNN forecasting model is obtained;After that,BP neural network is used to nonlinearly combine the predicted values of SFLA-CNN and LSTM models to obtain the final prediction results.The experimental results of water level prediction in Taihu Lake region of Jiangsu Province show that the combined model has effectively improved the prediction accuracy and better generalization ability compared with the existing models.