Water Quality Model Parameters Estimation Based on Particle Swarm Optimization Algorithm

Abstract:

Water quality parameter is the basic data of the river water quality mathematical model for forecasting river water quality status. In this paper, the parameter estimation problem of the analytical model, which is used to describe one-dimensional tracing test data of river streams with tracers instantaneously injected, is converted to the function optimization problem. And particle swarm optimization algorithm is applied to solve this problem. The experimental results show that the particle swarm optimization algorithm can estimate the water quality model parameter values regardless of whether the randomly sampling data has noise.

• Periodical: Advanced Materials Research (Volumes 610-613)
• Pages: 1925-1929
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.610-613.1925
• Online since: December 2012
• Authors: Wen Yuan Xu, Bing Xiang Liu*, Xing Xu, Na Hu, Hao Hu, Ying Zhu
• Keywords: Algorithm Application, Parameter Estimation, Particle Swarm Optimization (PSO), Water Quality Model, Water Quality Prediction

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