基于主要驱动因子筛选法和深度学习算法的浙江省动态需水量预测

收集了浙江省2000—2020年各用水行业需水量数据，采用基于Spearman秩相关分析的主要驱动因子筛选法筛选了影响各行业需水量的主要驱动因子，进而构造了改进的长短时记忆(LSTM)神经网络需水量预测模型，对各行业需水量进行动态滚动预测，并将改进LSTM模型的预测结果与传统单变量LSTM预测模型、卷积神经网络模型、支持向量回归模型的预测结果进行了对比。结果表明，基于主要驱动因子筛选法改进的LSTM模型能实时动态滚动预测各行业每年需水量，且预测结果精度高于其他3种模型。

Forecast of dynamic water demand in Zhejiang Province based on main driving factor screening method and deep learning algorithm

The water demand data of various water use industries in Zhejiang Province from 2000 to 2020 were collected, and the main driving factors affecting the water demand of each industry were screened using the main driving factor screening method based on Spearman rank correlation analysis. An improved long short-term memory (LSTM) neural network water demand prediction model was constructed to make dynamic rolling forecasts of the water demand of each industry, and the prediction results of the improved LSTM model were compared with those of the traditional univariate LSTM prediction model, convolutional neural network (CNN) model, and support vector regression (SVR) model. The results show that the LSTM model improved by the principal driving factor screening method can predict the annual water demand of each industry in real time and dynamically, and the prediction accuracy of the improved model is higher than that of the other three models.