摘要
Understanding the amount of instantaneous peak flow in watersheds is one of the most important factors that plays important role in planning and designing of projects related to water and river engineering. The purpose of this study is to compare the efficiency of artificial neural network and empirical methods for estimating instantaneous peak flow in Kharestan Watershed located northwest of Fars Province, Iran. For this purpose, 25 years of daily peak and instantaneous peak flow of Jamal Beig Hydrometric Station was considered. Then the estimation was done based on empirical methods including Fuller, Sangal and Fill-Steiner and artificial neural network and were compared based on RMSE and R2 . Results showed that estimation of artificial neural network is more accurate than empirical methods with RMSE = 13.710 and R2=0.942 which indicated the lower errors of artificial neural network method compared with empirical methods.
在水体工程及河流工程项目规划与设计中,瞬时顶峰流量是需要加以了解的最重要因素之一。本研究的目的是,在估算伊朗法尔斯省西北部Kharestan流域的瞬时顶峰流量时,对人工神经网络法与传统方法的功效进行对比。为此,采用了Jamal Beig水文站25年的日顶峰流量和瞬时顶峰流量数据。在Fuller、Sangal及Fill-Steiner经验方法以及人工神经网络方法的基础上进行了估算,并根据RMSE和R2进行了对比。结果显示,采用人工神经网络法的估算值比经验方法的更为精确,RMSE =13.710,R2= 0.942。这表明人工神经网络法比经验方法的误差更低。
