Dynamically dimensioned search algorithm for computationally efficient watershed model calibration

[1] A new global optimization algorithm, dynamically dimensioned search (DDS), is introduced for automatic calibration of watershed simulation models. DDS is designed for calibration problems with many parameters, requires no algorithm parameter tuning, and automatically scales the search to find good solutions within the maximum number of user-specified function (or model) evaluations. As a result, DDS is ideally suited for computationally expensive optimization problems such as distributed watershed model calibration. DDS performance is compared to the shuffled complex evolution (SCE) algorithm for multiple optimization test functions as well as real and synthetic SWAT2000 model automatic calibration formulations. Algorithms are compared for optimization problems ranging from 6 to 30 dimensions, and each problem is solved in 1000 to 10,000 total function evaluations per optimization trial. Results are presented so that future modelers can assess algorithm performance at a computational scale relevant to their modeling case study. In all four of the computationally expensive real SWAT2000 calibration formulations considered here (14, 14, 26, and 30 calibration parameters), results show DDS to be more efficient and effective than SCE. In two cases, DDS requires only 15–20% of the number of model evaluations used by SCE in order to find equally good values of the objective function. Overall, the results also show that DDS rapidly converges to good calibration solutions and easily avoids poor local optima. The simplicity of the DDS algorithm allows for easy recoding and subsequent adoption into any watershed modeling application framework.

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

[2]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[3]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[4]  John Doherty,et al.  Methodologies for calibration and predictive analysis of a watershed model, by John Doherty and John M. Johnston. Author's reply , 2003 .

[5]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[6]  S. Sorooshian,et al.  Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness , 1983 .

[7]  A. Griewank.  Generalized descent for global optimization , 1981 .

[8]  G. Bekey,et al.  A global optimization algorithm using adaptive random search , 1980 .

[9]  S. Sorooshian,et al.  Evaluation of Maximum Likelihood Parameter estimation techniques for conceptual rainfall‐runoff models: Influence of calibration data variability and length on model credibility , 1983 .

[10]  Henrik Madsen,et al.  Comparison of different automated strategies for calibration of rainfall-runoff models , 2002 .

[11]  Q. J. Wang.  THE GENETIC ALGORITHM AND ITS APPLICAYTION TO CALIBRATING CONCEPUTAL RAINFALL-RUNOFF MODELS , 1991 .

[12]  T. Gan,et al.  Automatic Calibration of Conceptual Rainfall-Runoff Models: Optimization Algorithms, Catchment Conditions, and Model Structure , 1996 .

[13]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[14]  Wyn L. Price,et al.  A Controlled Random Search Procedure for Global Optimisation , 1977, Comput. J..

[15]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[16]  George Kuczera,et al.  Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms , 1999 .

[17]  Willy Bauwens,et al.  Multiobjective autocalibration for semidistributed water quality models , 2003 .

[18]  M. Franchini,et al.  Global optimization techniques for the calibration of conceptual rainfall-runoff models , 1998 .

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

[20]  R. Ibbitt,et al.  Systematic parameter fitting for conceptual models of catchment hydrology , 1970 .

[21]  Zelda B. Zabinsky,et al.  A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems , 2005, J. Glob. Optim..

[22]  V. Singh,et al.  Mathematical Modeling of Watershed Hydrology , 2002 .

[23]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[24]  S. Sorooshian,et al.  Effective and efficient algorithm for multiobjective optimization of hydrologic models , 2003 .

[25]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[26]  Christine A. Shoemaker,et al.  Watershed modeling of the Cannonsville Basin using SWAT2000: Model , 2004 .

[27]  P. E. O'connell,et al.  River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood , 1970 .

[28]  G. Kuczera.  Efficient subspace probabilistic parameter optimization for catchment models , 1997 .

[29]  Charles H. Luce,et al.  Parameter identification for a runoff model for forest roads , 1994 .

[30]  D. Ackley.  A connectionist machine for genetic hillclimbing , 1987 .

[31]  Andy J. Keane,et al.  A brief comparison of some evolutionary optimization methods , 1996 .

[32]  Soroosh Sorooshian,et al.  Multi-objective global optimization for hydrologic models , 1998 .

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

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

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

[36]  Joseph B. Hughes.  School of Civil and Environmental Engineering Strategic Plan: The Path Through 2011 , 2008 .