Title: Study on Monthly Runoff Prediction Based on Four Methods
Abstract: Runoff forecast is very important to the rational utilization and distribution of water resources.According to the monthly runoff data from Zhuangtou Hydrologic Station of Beiluo River and Zhangjiashan Hydrologic Station of Jinghe River,which are two branches of Weihe River in loess plateau,the threshold auto-regressive model,neural network model,variance analysis extrapolation as well as the seasonal level model are used to predict the monthly runoff,observe the similation results and find their advantages and disadvantages.The results show that in a dry season,the eligible rates for runoff forecast by using the seasonal level model for the two stations are both 100%.Using the variance analysis extrapolation for Zhuangtou Station and Zhangjiashan Station,the eligible rates are 90% and 80% respectively.The eligible rates for runoff forecast by using the threshold auto-regressive model for the two stations are both 80%.While in a flood season,the eligible rates for runoff forecast by using the neural network model for the two stations are both 100%.This study show that the seasonal level model is applicable to the runoff forecast in a dry season.And the neural network model is suitable for the runoff forecast in a flood season,and both models have a good simulation accuracy.
Publication Year: 2010
Publication Date: 2010-01-01
Language: en
Type: article
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Cited By Count: 1
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