摘要
雅砻江流域地面气象站点不足、分布不均,难以获得精确的流域面降雨资料,加之传统中长期径流预报模型泛化能力有限,中长期径流预报存在较大瓶颈。充分考虑流域水库间的物理联系,基于上下游水库流量变化在时空上的相似性,对1957年~2020年锦屏一级水库和二滩水库的历史月径流数据进行主成分分析,使用BP人工神经网络、随机森林和支持向量回归3种机器学习方法建立3种径流预报模型,通过决定系数R^(2),合格率Q R以及平均相对误差MRE三项指标构成的评价体系对预测结果进行评估。结果表明,上游水库对于下游水库的入库流量具有显著影响,且3种模型在二滩水库中长期径流预报上均具有较好的预报效果(R^(2)>0.8、Q R>0.7、MRE<0.2)。随机森林模型模拟效果整体优于BP人工神经网络和支持向量回归模型,3种模型均具有较好的实用性,能为流域水资源精细化调度及科学管理提供数据基础。
As the limited number and uneven distribution of surface meteorological stations in Yalong River Basin,the precise rainfall data in the basin is difficult to obtain,and coupled with the limited generalization ability of traditional medium and long-term runoff forecasting models,there is a big bottleneck in medium and long-term runoff forecasting.Considering the temporal and spatial similarity of flow variation between upstream and downstream reservoirs,the principal component analysis(PCA)is conducted on the historical monthly runoff data of Jinping I Reservoir and Ertan Reservoir from 1957 to 2020,and three runoff prediction models are established by using three machine learning methods of BP artificial neural network,random forest and support vector regression,respectively.The prediction results of three runoff prediction models are evaluated by three indicators of coefficient of determination(R^(2)),pass rate(Q R),and mean relative error(MRE).The results show that the upstream reservoir has a significant influence on the inflow of the downstream reservoir,and all the three models exhibit excellent prediction performance in the medium and long-term runoff prediction of Ertan Reservoir(R^(2) greater than 0.8,Q R greater than 0.7 and MRE less than 0.2).In general,the random forest model demonstrates superior simulation performance to BP artificial neural network and support vector regression model,and all the three models have good practicability,which can provide a data basis for the refined allocation and scientific management of river basin water resources.
作者
李世林
黄炜斌
陈枭
周开喜
钟璐
曾宏
LI Shilin;HUANG Weibin;CHEN Xiao;ZHOU Kaixi;ZHONG Lu;ZENG Hong(College of Water Resources and Hydropower,Sichuan University,Chengdu 610065,Sichuan,China;Zhenxiong County Water Bureau,Zhaotong 657200,Yunnan,China;Southwest Subsection of State Grid,Chengdu 610041,Sichuan,China)
出处
《水力发电》
CAS
2024年第5期16-20,121,共6页
Water Power
基金
国家重点研发计划(2018YFB0905204)
四川省科技计划(2022YFG0292)。
关键词
径流预报
中长期
主成分分析
BP人工神经网络
随机森林
支持向量回归
二滩水库
runoff forecast
medium and long-term
principal component analysis
BP artificial neural network
random forest
support vector regression
Ertan Reservoir
作者简介
李世林(1999-),男,四川巴中人,硕士研究生,研究方向为水文水资源;通信作者:黄炜斌.
