Title :
Rainfall-Runoff Modelling using Data Driven and Statistical Methods
Author :
Khan, Saadat Ayub ; See, Linda
Abstract :
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.
Keywords :
rain; rivers; statistical analysis; 24 hours; 6 hours; data driven methods; multiple linear regression; rainfall runoff modeling; river Ouse catchment; statistical methods; Distributed decision making; Floods; Hydrologic measurements; Linear regression; Neural networks; Predictive models; Rivers; Sea measurements; Statistical analysis; Testing;
Conference_Titel :
Advances in Space Technologies, 2006 International Conference on
Conference_Location :
Islamabad
Print_ISBN :
1-4244-0515-7
Electronic_ISBN :
1-4244-0515-7
DOI :
10.1109/ICAST.2006.313789
