Automatic Calibration of Hydrologic Models With Multi‐Objective Evolutionary Algorithm and Pareto Optimization¹

Abstract

In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade‐offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi‐objective evolutionary algorithm (MOEA) and Pareto ordering optimization in the automatic calibration of the Soil and Water Assessment Tool (SWAT), a process‐based, semi‐distributed, and continuous hydrologic model. The nondominated sorting genetic algorithm II (NSGA‐II), a fast and recent MOEA, and SWAT were called in FORTRAN from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. A total of 139 parameter values were simultaneously and explicitly optimized in the calibration. The calibrated SWAT model simulated well the daily streamflow of the Calapooia watershed for a 3‐year period. The daily Nash‐Sutcliffe coefficients were 0.86 at calibration and 0.81 at validation. Automatic multi‐objective calibration of a complex watershed model was successfully implemented using Pareto ordering and MOEA. Future studies include simultaneous automatic calibration of water quality and quantity parameters and the application of Pareto optimization in decision and policy‐making problems related to conflicting objectives of economics and environmental quality.

Multiobjective Automatic Parameter Calibration of a Hydrological Model

“…The two objectives were simultaneously optimized; they were not aggregated in this study. In the field of hydrological model calibration, the NSGA-II, Multiobjective Complex Evolution (MOCOM) algorithm and the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm are widely used [8,[46][47][48][49][50][51][52][53][54][55][56][57][58][59]. A set of Pareto optimal solutions (non-dominated parameter sets) was obtained from the model.…”

Impact of the Agricultural Research Service Watershed Assessment Studies on the Conservation Effects Assessment Project Cropland National Assessment

“…A special issue of the Journal of Soil and Water Conservation (Richardson et al 2008) was devoted to monitoring and model applications at all 14 benchmark watersheds. Objective 4 integrates data from the watersheds and the model results into economic analysis for decision support Confesor and Whittaker 2007). Objective 5, Regionalization of Models, captures legacy computer models into modular packages using collaborative object-oriented modeling system (David et al 2013) methods to facilitate development of models applicable in specific regions of the United States.…”

Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: An offline evaluation using the MOSCEM optimization algorithm and field observations

“…Designed for the automatic calibration of hydrological models, the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm of Vrugt et al (2003) provides a systematic and unbiased tool for such a purpose. Of particular interest is its ability to: (1) objectively sample the entire parameter space rather than a discrete predefined set of values, and (2) optimize with respect to several criteria (multi-objective optimization), thereby providing some insight into the trade-offs which control the modelled SEB (multi-objective optimization identifies situations where improving one criteria is possible only at the expense of another: Gupta et al, 1998;Khu and Madsen, 2005;Confesor and Whittaker, 2007;Shafii and De Smedt, 2009). The tradeoff surfaces, composed of all parameter values leading to an optimum compromise in the performance of the modelled fluxes, are referred to as Pareto fronts to highlight the nonuniqueness of the solution .…”