嘉陵江中游段中长期径流预报研究  

Study on Medium and Long-term Runoff Forecasting Methods for the Middle Reaches of Jialing River

作　　者：李博[1] 李克锋[1] 王莉[2] 

机构地区：[1]四川大学水力学与山区河流开发保护国家重点实验室,四川成都610065 [2]中国水电顾问集团成都勘测设计研究院,四川成都610072

出　　处：《水力发电》2011年第8期7-10,共4页Water Power

摘　　要：通过对嘉陵江流域中游段的径流特性及变化规律进行研究,应用目前较为成熟的人工神经网络模型、最近邻抽样回归模型、自回归模型和均生函数模型,对嘉陵江流域中游段年径流进行预报。实例分析和预测结果比较表明:人工神经网络模型与最近邻抽样回归模型能更好地预测嘉陵江中游段的年径流。Through studying the characteristics and variations of runoff in the middle reaches of Jialing River, annual runoIi forecasting are conducted with artificial neural network model, nearest neighbor bootstrapping regressive model, automatic regressive model and mean generating function model. Comparison of the predicting results shows that the artificial neural network model and nearest neighbor bootstrapping regressive model can produce a better overall outcome for annual runoff forecasting in the middle reaches of Jialing River.

关 键 词：中长期 径流 水文预报 模型 嘉陵江 

分 类 号：P338.2[天文地球—水文科学]