Use of Gene Expression Programming in regionalization of flow duration curve |
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| Affiliation: | 1. Water Resources and Glaciology Section, Global Change Impact Studies Centre (GCISC), Pakistan;2. Department of Civil and Environmental Engineering, The University of Auckland, New Zealand;1. DICA, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy;2. Department of Geoscience and Engineering, Delft University of Technology, Delft, The Netherlands;1. 302 ABNR Bldg., Dept. of Soil, Environ. and Atmos. Sciences, University of Missouri, Columbia, MO 65211, United States;2. 203 ABNR Bldg., The Center for Agroforestry, University of Missouri, Columbia, MO 65211, United States;3. USDA-ARS Cropping Systems and Water Quality Research Unit, 241 Ag. Eng. Bldg., University of Missouri, Columbia, MO 65211, United States;4. 251 Ag. Eng. Bldg., Dept. of Biological Engineering, University of Missouri, Columbia, MO 65211, United States |
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| Abstract: | In this paper, a recently introduced artificial intelligence technique known as Gene Expression Programming (GEP) has been employed to perform symbolic regression for developing a parametric scheme of flow duration curve (FDC) regionalization, to relate selected FDC characteristics to catchment characteristics. Stream flow records of selected catchments located in the Auckland Region of New Zealand were used. FDCs of the selected catchments were normalised by dividing the ordinates by their median value. Input for the symbolic regression analysis using GEP was (a) selected characteristics of normalised FDCs; and (b) 26 catchment characteristics related to climate, morphology, soil properties and land cover properties obtained using the observed data and GIS analysis. Our study showed that application of this artificial intelligence technique expedites the selection of a set of the most relevant independent variables out of a large set, because these are automatically selected through the GEP process. Values of the FDC characteristics obtained from the developed relationships have high correlations with the observed values. |
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| Keywords: | Hydrology Artificial intelligence Catchment Non-linear regression |
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