Multiobjective particle swarm optimization for parameter estimation in hydrology

Abstract

[1] Modeling of complex hydrologic processes has resulted in models that themselves exhibit a high degree of complexity and that require the determination of various parameters through calibration. In the current application we introduce a relatively new global optimization tool, called particle swarm optimization (PSO), that has already been applied in various other fields and has been reported to show effective and efficient performance. The PSO approach initially dealt with a single-objective function but has been extended to deal with multiobjectives in a form called multiobjective particle swarm optimization (MOPSO). The algorithm is modified to account for multiobjective problems by introducing the Pareto rank concept. The new MOPSO algorithm is tested on three case studies. Two test functions are used as the first case study to generate the true Pareto fronts. The approach is further tested for parameter estimation of a well-known conceptual rainfall-runoff model, the Sacramento soil moisture accounting model having 13 parameters, for which the results are very encouraging. We also tested the MOPSO algorithm to calibrate a three-parameter support vector machine model for soil moisture prediction.

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

“…Multi-objective global optimization (Cheng et al, 2002;Gill et al, 2006;Rajesh and Michael, 2008;Kraue et al, 2011) has been developed because any single-objective function, no matter how carefully chosen, may not adequately measure the ways in which a model fails to match important characteristics of observed data (Li et al, 2010). Considerable improvement of model performance may be achieved through parameters calibrated by such optimization.…”

“…PSO was proposed by Kennedy and Eberhart (1995) based on the analogy of swarming animals and is a simple and powerful heuristic method for solving nonlinear, nondifferential and multimodal optimization problems. Since Gill et al (2006) used PSO for parameter estimation in hydrology, many researchers have presented various types of PSO for hydrologic model calibration (Jiang et al, 2007(Jiang et al, , 2010Zhang et al, 2009;Kuok et al, 2010;Kraue et al, 2011). PSO approach has many computational advantages over traditional evolutionary computing, such as rapid convergence (Jiang et al, , 2007Chau, 2007).…”

“…Many optimization methods have been proposed and improved to select model parameters using calibration. Foremost are the Genetic Algorithm (GA) (Wang, 1991(Wang, , 1997Cheng et al, 2002), SCE-UA (Duan et al, 1992(Duan et al, , 1994Vrugt et al, 2003aVrugt et al, , 2003b and particle swarm optimization (PSO) (Gill et al, 2006;Zhang et al, 2009;Jiang et al, 2010;Kuok et al, 2010;Kraue et al, 2011). GA is a highly parallel and adaptive optimization algorithm, but it suffers premature convergence to become trapped in local optima (Jiang et al, 2010).…”

Comparative assessment of six automatic optimization techniques for calibration of a conceptual rainfall—runoff model

“…Two recent applications in hydrology are the parameter estimation of the Sacramento soil moisture accounting model (Gill et al, 2006) and in the training algorithm for an artificial neural network (ANN) in stage prediction of a river in Hong Kong (Chau, 2006). …”

Improved particle swarm algorithm for hydrological parameter optimization

“…PSO has gained popularity lately and has been applied to wide applications in different fields [15][16][17][18][19]. Since the frame of this algorithm is relatively simple and the operation speed is very fast, PSO has also been applied in hydrological parameter optimization [20,21]. However, it points out that although PSO shows significant performance in the initial iterations, there still exist some problems.…”