Effective multiobjective hydrologic model calibration
نویسندگان
چکیده
Effective multiobjective hydrologic model calibration P. Reed et al. Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Effective multiobjective hydrologic model calibration P. Reed et al. Abstract This study provides a comprehensive assessment of state-of-the-art evolutionary multi-objective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic 5 Algorithm-II (ε-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hy-drologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a com-10 putationally intensive integrated model application in the Shale Hills watershed in Penn-sylvania. A challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 is an excellent benchmark algorithm for multiobjective hydrologic model calibration. SPEA2 attained competitive to superior 15 results for most of the problems tested in this study. ε-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration.
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How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?
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In a recent paper by Tang, Reed and Wagener (2006, hereafter referred to as TRW) a comparison assessment was presented of three state-of-the-art evolutionary algorithms for multiobjective calibration of hydrologic models. Through three illustrative case studies, TRW demonstrate that the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Epsilon Dominance Nondominated Sorted Genetic Algorithm ...
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