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This paper outlines the application of multiple linear regression and three different data-driven modeling techniques to river level forecasting for the river Ouse catchment in northern England. Lead times of 6 and 24 hours ahead were modelled. The results show that the data driven approaches generally outperformed the statistical approach and that M5 model trees have great potential for the development of transparent river level forecasting models.
Date of Conference: 2-3 Sept. 2006