基于SSA-LSTM的日径流预测  

Daily Runoff Prediction Based on SSA-LSTM

作　　者：孟令敏 唐加山[1] Lingmin Meng;Jiashan Tang(School of Science,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu)

机构地区：[1]南京邮电大学理学院,江苏南京

出　　处：《建模与仿真》2024年第6期5857-5871,共15页Modeling and Simulation

摘　　要：关于径流的中长期预测始终是水文预报所研究的重难点问题。为提高日径流的预测精度,提出一种基于麻雀搜索算法(SSA)和长短期记忆神经网络(LSTM)的日径流预测模型,即利用SSA算法优化LSTM模型的超参数后,对日径流进行预测。结果显示:对于特拉华河在霍巴特的西分支、纽约霍巴特东南的小镇小溪、特拉华河西分支从德里上游和德里附近的小特拉华河的日径流,基于SSA-LSTM模型的预测准确率分别为99.9994%、99.9977%、99.9991%、99.9997%,相较于LSTM模型,分别提升了0.014%、0.004%、0.011%、0.008%。该模型其他指标平方绝对百分比误差(MAPE)、根均方误差(RMSE)、平均绝对误差(MAE)相较于对照模型也有明显下降。研究表明,利用SSA-LSTM模型预测日径流量具有良好的准确性,可以为日径流量的预测提供依据。The mid-long term prediction of runoff is always a key and difficult problem in hydrological fore-casting.In order to improve the prediction accuracy of daily runoff,a daily runoff prediction model based on Sparrow search algorithm(SSA)and Long Short-Term Memory model(LSTM)was pro-posed.The daily runoff is forecasted.The results show that for the western branch of Delaware River in Hobart,the Small Town Creek in southeastern new Hobart,and the western branch of Del-aware River,daily runoff from the Little Delaware River upstream and near Delhi,the prediction accuracy of SSA-LSTM model was 99.9994%,99.9977%,99.9991%,99.9997%respectively,which was 0.014%,0.004%,0.011%,0.008%higher than that of LSTM model.Compared with the control model,the square absolute percentage error(Mape),root mean square error(RMSE)and mean ab-solute error(MAE)of other indexes of the model also decreased significantly.The study shows that the SSA-LSTM model has good accuracy in predicting daily runoff,which can provide a basis for the prediction of daily runoff.

关 键 词：日径流 麻雀搜索算法 长短期记忆神经网络 

分 类 号：P33[天文地球—水文科学]