Comparative Analysis of Data-Driven and GIS-Based Conceptual Rainfall-Runoff Model
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 1
Abstract
Modeling of the rainfall-runoff process is important in hydrology. Historically, researchers relied on conventional deterministic modeling techniques based either on the physics of the underlying processes, or on the conceptual systems which may or may not mimic the underlying processes. This study investigates the suitability of a conceptual technique along with a data-driven technique, to model the rainfall-runoff process. The conceptual technique used is based on the Xinanjiang model coupled with geographic information system (GIS) for runoff routing and the data-driven model is based on genetic programming (GP), which was used for rainfall-runoff modeling in the recent past. To verify GP’s capability, a simple example with a known relation from fluid mechanics is considered first. For a small, steep-sloped catchment in Hong Kong, it was found that the conceptual model outperformed the data-driven model and provided a better representation of the rainfall-runoff process in general, and better prediction of peak discharge, in particular. To demonstrate the potential of GP as a viable data-driven rainfall-runoff model, it is successfully applied to two catchments located in southern China.
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Acknowledgments
This study was partially supported by the Hong Kong Research Grants Council Group Research Project, Grant No. UNSPECIFIEDRGC/CA/HKU 2/98C. The writers thank Dr. Zhou Maichun for the kind help extended during the course of this study. The data set for the Shanqiao catchment is taken from the yearly hydrological reports of the Guangdong Provincial General Hydrological Station. Their cooperation is also acknowledged. The writers also thank DHI Water and Environment for providing the GP software, GPKernel.
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Received: Aug 14, 2003
Accepted: Mar 12, 2005
Published online: Jan 1, 2006
Published in print: Jan 2006
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