Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models

Abstract The Genetic Algorithm (GA) is often associated with local search optimisation techniques in the calibration process of Conceptual Rainfall-Runoff Models (CRRMs) (Wang, 1991; Franchini, 1996), i.e. the GA is used for approaching the region encompassing the global solution and then its results are used as a starting point for the local optimizer in the subsequent “fine-tuning” process. However, the GA can be formulated in very many ways. This study analyses various GA structures and their robustness and efficiency. In addition, a sensitivity analysis of the various schemes to their own parameters is performed. The analysis is conducted using an 11-parameter CRRM, called A Distributed Model (ADM), applied to both a theoretical case without model and data errors and two cases of the real world in which there are both model and data errors. Finally, assuming the same role as the GA for the “Pattern Search” (PS) method in a two-step optimisation technique (Hendrickson et al., 1988), the results of the ...

[1]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[2]  S. Ranjithan,et al.  Using genetic algorithms to solve a multiple objective groundwater pollution containment problem , 1994 .

[3]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[4]  R. Ibbitt,et al.  Fitting Methods for Conceptual Catchment Models , 1971 .

[5]  D. McKinney,et al.  Genetic algorithm solution of groundwater management models , 1994 .

[6]  L. Darrell Whitley,et al.  Optimizing Neural Networks Using FasterMore Accurate Genetic Search , 1989, ICGA.

[7]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[8]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  M. Franchini.  Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models , 1996 .

[11]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  L. L. Rogers,et al.  Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling , 1994 .

[14]  Jene Diane Hendrickson,et al.  Comparison of Newton-type and direct search algorithms for calibration of conceptual rainfall-runoff models , 1988 .

[15]  S. Sorooshian,et al.  A maximum likelihood criterion for use with data collected at unequal time intervals , 1988 .

[16]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[17]  S. Liong,et al.  Peak-flow forecasting with genetic algorithm and SWMM , 1995 .

[18]  Brian J. Wagner,et al.  Sampling Design Methods For Groundwater Modeling Under Uncertainty , 1995 .

[19]  P. R. Johnston,et al.  Parameter optimization for watershed models , 1976 .

[20]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[21]  J. Eheart,et al.  Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem , 1995 .

[22]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[23]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.

[24]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[25]  Soroosh Sorooshian,et al.  Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model , 1993 .