基于LCD-SSA-BiLSTM模型的月径流预测研究
Research on Monthly Runoff Prediction Method Based on LCD-SSA-BiLSTM Model
任智晶 1赵雪花 1郭秋岑 1付兴涛1
作者信息
- 1. 太原理工大学水利科学与工程学院,山西 太原 030024
- 折叠
摘要
径流预测在水资源优化配置和防汛抗旱方面发挥着重要作用.但径流序列非平稳会导致预测误差及峰值预测误差较大,因此提出了基于局部特征尺度分解(LCD)、麻雀搜索算法(SSA)和双向长短期记忆神经网络(BiLSTM)的组合预测模型(LCD-SSA-BiLSTM),以提高非平稳径流序列的预测精度.以汾河上游 4 个站点(汾河水库站、上静游站、兰村站和寨上站)为研究对象开展月径流序列预测研究,采用纳什效率系数、平均绝对误差、均方根误差、合格率 4 个评价指标对预测结果进行定量评价.结果表明,LCD-SSA-BiLSTM模型的平均绝对误差为 10.346×104～124.629×104 m3,均方根误差为 19.416×104～191.284×104 m3,纳什效率系数为 0.975～0.988,4 个水文站的合格率均在 90%及以上,预测精度为甲级,与单一BiLSTM、EMD-BiL-STM、LCD-BiLSTM及 EMD-SSA-BiLSTM模型相比预测效果更好,因此 LCD-SSA-BiLSTM模型是预测非平稳月径流序列的有效方法.
Abstract
Runoff prediction plays an important role in the optimal allocation of water resources and flood control and drought relief.To solve the problem of large prediction errors caused by non-smoothness and extreme values of runoff se-ries and improve the prediction accuracy,this paper proposes a combined prediction model(LCD-SSA-BiLSTM)based on local characteristic-scale decomposition(LCD),sparrow search algorithm(SSA)and bi-directional long short-term mem-ory(BiLSTM)to study the monthly runoff series of four stations in the upper reaches of Fenhe River(Fenhe Reservoir Station,Shangjingyou Station,Lancun Station and Zhaishang Station).Nash efficiency coefficient(NNSE),mean abso-lute error(MMAE),root mean square error(RRMSE),and qualification rate(QQR)are used to quantitatively evaluate the prediction results.Compared with the single BiLSTM model,EMD-BiLSTM model,LCD-BiLSTM model and EMD-SSA-BiLSTM model,the results show that the LCD-SSA-BiLSTM model has higher prediction accuracy with MMAE of 10.346×104-124.629×104 m3,RRMSE of 19.416×104-191.284×104m3,NNSE of 0.975-0.988,and the QQR of all four hydrological stations were 90%and above,and the prediction accuracy was grade A.Thus,the LCD-SSA-BiLSTM mod-el is an effective method to predict non-stationary monthly runoff series.
关键词
汾河上游/BiLSTM模型/LCD/月径流预测Key words
upper reaches of the Fenhe River/BiLSTM model/LCD/monthly runoff prediction引用本文复制引用
基金项目
国家自然科学基金(52279020)
山西省科技创新人才团队专项(202204051002027)
山西省基础研究计划(202203021221050)
山西省水利科学技术研究与推广项目(2023ZF15)
出版年
2024
NETL
NSTL
维普
万方数据