Effective and efficient algorithm for multiobjective optimization of hydrologic models

TLDR

Hydrologic model calibration often fails when relying on a single objective function, as it cannot capture all important data characteristics. The paper introduces MOSCEM, an efficient Markov Chain Monte Carlo sampler designed to solve multiobjective optimization problems in hydrologic models. MOSCEM extends SCEM‑UA by incorporating Pareto dominance into a Markov Chain Monte Carlo framework, using multiple objective functions to evolve a population toward a Pareto set, and its performance was benchmarked against MOCOM‑UA on three hydrologic case studies.

Abstract

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.