Particle Swarm Optimization for Calibrating Stream Water Quality Model

Parameter estimation of water quality model is generally obtained through the comparison of measured histories and model predictions of water bodies. In this paper, we present a new calibration tool of solving the parameter estimation problem by using particle swarm optimization (PSO) technique, which is a swarm intelligence method developed to perform heuristic direct search in a continuous parameter space without requiring any derivative estimation. To demonstrate the scheme, experiments with synthetic data were conducted on one-dimensional steady state stream water quality model. The experimental results show that the PSO approach still performs very well even if the exact solution is corrupted by noise.