基于PSO-LSTM模型的平原河网汛期水位预测  

Prediction of water level of plain river network during flood season based on PSO-LSTM model

作　　者：郭明辰 张润润[1] 闻余华 GUO Mingchen;ZHANG Runrun;WEN Yuhua(College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China;Jiangsu Hydrology and Water Resources Survey Bureau,Nanjing 210029,China)

机构地区：[1]河海大学水文水资源学院,江苏南京210098 [2]江苏省水文水资源勘测局,江苏南京210029

出　　处：《水利水电科技进展》2024年第6期64-70,共7页Advances in Science and Technology of Water Resources

基　　金：中央高校基本科研业务费专项资金资助项目(B220202035)。

摘　　要：基于水位、流量、降水量长序列数据资料,构建了采用粒子群优化(PSO)算法进行超参数寻优的长短期记忆(LSTM)神经网络模型(PSO-LSTM模型),对位于江南运河苏州吴江段平原河网中心的平望站的汛期水位进行了预见期为1~3 d的短期预报,并与基于PSO算法的支持向量机(SVM)、随机森林(RF)、卷积神经网络(CNN)以及门控循环单元(GRU)模型的水位预测结果进行了对比,同时探究了水利工程对水位预报精度的影响。结果表明:对于预见期为1~3 d的短期预报,PSO-LSTM模型具有较高的预测精度,但随着预见期的增长,预测精度逐渐降低;相较于PSO-SVM、PSO-RF、PSO-CNN和PSO-GRU模型,PSO-LSTM模型的平均绝对百分比误差更低,预测效果更好;PSO-LSTM模型能够较有效地进行汛期平原河网的水位预测,且加入水利工程等人工调控影响因素能够提升水位预测效果。Based on the long-term series data of water level,flow,and precipitation,a long short-term memory(LSTM)neural network model using the particle swarm optimization(PSO)algorithm(PSO-LSTM model)for hyperparameter optimization was constructed to provide short-term forecasts with forecast periods of 1 to 3 days for the water level during the flood season at Pingwang Station,the center of the plain river network in the Wujiang section of the Jiangnan Canal in Suzhou.The water level prediction results were compared with those from water level prediction models based on the PSO algorithm,including support vector machine(SVM),random forests(RF),convolutional neural networks(CNN),and gated recurrent units(GRU).The effects of water conservancy projects on the prediction accuracy of water level were investigated.The results show that the PSO-LSTM model has high prediction accuracy for short-term forecasts with forecast periods of 1 to 3 days,but the prediction accuracy gradually decreases with the growth of the forecast period.Compared with the PSO-SVM,PSO-RF,PSO-CNN,and PSO-GRU models,the PSO-LSTM model has a lower mean absolute percentage error and better prediction efficiency.The PSO-LSTM model can efficiently predict water level of the plain river network during the flood season,and the addition of artificial regulatory influences such as water conservancy projects can improve the water level prediction efficiency.

关 键 词：汛期水位预测 粒子群算法 长短期记忆 机器学习 平原河网 

分 类 号：P338[天文地球—水文科学]