A hybrid artificial intelligence model for river flow forecasting

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• 作者：Carlos H. Fajardo Toro ; Silvana G¨®mez Meire ; Juan F. G¨¢lvez ; Florentino Fdez-Riverola
• 关键词：River flow forecasting ; Hydrologic models ; Black-box approaches ; Case-based reasoning ; Hybrid forecasting system
• 刊名：Applied Soft Computing Journal
• 出版年：2013
• 出版时间：August, 2013
• 年：2013
• 卷：13
• 期：8
• 页码：3449-3458
• 全文大小：2268 K

文摘

A hybrid hydrologic estimation model is presented with the aim of performing accurate river flow forecasts without the need of using prior knowledge from the experts in the field. The problem of predicting stream flows is a non-trivial task because the various physical mechanisms governing the river flow dynamics act on a wide range of temporal and spatial scales and almost all the mechanisms involved in the river flow process present some degree of nonlinearity. The proposed system incorporates both statistical and artificial intelligence techniques used at different stages of the reasoning cycle in order to calculate the mean daily water volume forecast of the Salvajina reservoir inflow located at the Department of Cauca, Colombia. The accuracy of the proposed model is compared against other well-known artificial intelligence techniques and several statistical tools previously applied in time series forecasting. The results obtained from the experiments carried out using real data from years 1950 to 2006 demonstrate the superiority of the hybrid system.