基于WT-SA-LSTM的降水量预测  

Precipitation Prediction Based on WT-SA-LSTM

作　　者：田瑞杰 花磊 崔骥 

机构地区：[1]南京信息工程大学数学与统计学院,江苏 南京 [2]苏州博纳讯动软件有限公司,江苏 苏州

出　　处：《运筹与模糊学》2023年第6期7839-7850,共12页Operations Research and Fuzziology

摘　　要：监测和预测降水量对于农业、水资源管理、气象灾害预警等方面都至关重要。本文利用1991~2020年江西省九江市10个地面气象观测站月降水量实测数据,建立SARIMA、随机森林、LSTM降水量预测模型。结果表明,LSTM模型的MSE、MAPE、R方分别为0.0101、7.76%、0.7971,比SARIMA、随机森林误差低;其次,在LSTM模型的基础上,加入小波变换理论和模拟退火算法,将三者结合并运用在月降水量预测中得到WT-SA-LSTM模型,该模型MSE、MAPE、R方分别为0.0082、5.78%、0.8454,预测效果比单一的神经网络模型误差MSE、MAPE分别降低19.8%、25%,R方提高了4.9%。Monitoring and forecasting precipitation holds great importance in various fields including agriculture, water management, and meteorological disaster warning. This paper focuses on the measured monthly precipitation data from 1991 to 2020 of 10 surface meteorological observa-tion stations in Jiujiang City, Jiangxi Province. The study aims to establish a precipitation prediction model using SARIMA, random forest, and LSTM. The results showed that the MSE, MAPE, and R-squared of the LSTM model were 0.0101, 7.76%, and 0.7971, respectively, which were lower than those of the SARIMA and random forest models. Secondly, based on the LSTM model, the WT-SA-LSTM model was developed by combining wavelet transformation theory and simulated annealing algorithm, and applied to monthly precipitation forecasting. The MSE, MAPE, and R-squared of the WT-SA-LSTM model were 0.0082, 5.78%, and 0.8454, respectively, and the predictive performance was better than that of the single neural network model with a decrease of 19.8% and 25% in MSE and MAPE, respectively, and an increase of 4.9% in R-squared.

关 键 词：降水量 小波分析 模拟退火 LSTM 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]