Effective and efficient algorithm for multiobjective optimization of hydrologic models

Practical experience with the calibration of hydrologic models suggests that any single‐objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM‐UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single‐objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM‐UA algorithm is compared with the original MOCOM‐UA algorithm for three hydrologic modeling case studies of increasing complexity.

[1]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

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

[3]  E. L. Peck,et al.  Catchment modeling and initial parameter estimation for the National Weather Service River Forecast System , 1976 .

[4]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[5]  George Kuczera,et al.  On the relationship between the reliability of parameter estimates and hydrologic time series data used in calibration , 1982 .

[6]  G. Kuczera.  Improved parameter inference in catchment models: 2. Combining different kinds of hydrologic data and testing their compatibility , 1983 .

[7]  J. D. Schaffer,et al.  Multiple Objective Optimization with Vector Evaluated Genetic Algorithms , 1985, ICGA.

[8]  W. Price.  Global optimization algorithms for a CAD workstation , 1987 .

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

[10]  Richard P. Hooper,et al.  A multisignal automatic calibration methodology for hydrochemical models: A case study of the Birkenes Model , 1988 .

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

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

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

[15]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .

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

[17]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

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

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

[20]  P. Heuberger,et al.  Calibration of process-oriented models , 1995 .

[21]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[22]  George Kuczera,et al.  Assessment of hydrologic parameter uncertainty and the worth of multiresponse data , 1998 .

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

[24]  L. Bastidas,et al.  Parameter estimation for hydrometeorological models using multi-criteria methods , 1998 .

[25]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

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

[27]  W. J. Shuttleworth,et al.  Parameter estimation of a land surface scheme using multicriteria methods , 1999 .

[28]  Soroosh Sorooshian,et al.  Sensitivity analysis of a land surface scheme using multicriteria methods , 1999 .

[29]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[30]  Henrik Madsen,et al.  Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. , 2000 .

[31]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[32]  Soroosh Sorooshian,et al.  Toward improved streamflow forecasts: value of semidistributed modeling , 2001 .

[33]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[34]  Luis A. Bastidas,et al.  Calibrating a land surface model of varying complexity using multicriteria methods and the Cabauw dataset , 2002 .

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

[36]  Hoshin V. Gupta,et al.  Exploring the relationship between complexity and performance in a land surface model using the multicriteria method , 2002 .