Genetic Algorithm Applied To The Calibration Of
A Rainfall-infiltration-runoff Model
Author(s)
R. Chérif, J. L. Robert & R. Lagacé
Abstract
Standard optimization methods are generally moderately robust and easily
diverge due to the form of the objective function. A genetic algorithm (GA) is
used in this work to solve the calibration problems of a coupled model
rainfall-infiltration-runoff (RIR). The model parameters are those related to
Green and Ampt model infiltration in its general form (hydraulic conductivity
and B). Experiments are done in the laboratory to collect rainfall runoff data,
then the infiltration capacities versus infiltration heights are plotted and the
scattered points are fitted to a linear regression, in order to deduce the model
parameters. Experimental data are used to test the efficiency and the robustness
of the genetic algorithm (GA) (using different crossovers). Moreover, the RIR
model parameters values estimated by GA optimization are compared to those
obtained by experimental fitting strategy. Finally, synthesized data are used to
validate the optimization results.
Keywords: rainfall-runoff, infiltration, hydraulic conductivity, model,
optimization, genetic algorithm, crossover.
1 Introduction
Global rainfall-runoff models (RR) commonly use a number of parameters. Most
of these parameters are estimated by calibration strategies [1], so the model
results will be dependent of its hypothesis, inputs and parameters. These
parameters are generally draining coefficients of reservoirs. However, it will be
more adequate to estimate some physical parameters from calibration with runoff
Keywords
rainfall-runoff, infiltration, hydraulic conductivity, model,
optimization, genetic algorithm, crossover.
Related Book
Other papers in this volume
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