title: Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM (1,1) model.
abstract: An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guanting reservoir inlet and outlet (China). The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What's more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.
authors.affiliation: School of Economics and Management, Beihang University, Beijing 100191, China. Electronic address: anyanmog@163.com.
authors.forename: Yan
authors.initials: Y
authors.lastname: An
authors.affiliation: School of Economics and Management, Beihang University, Beijing 100191, China. Electronic address: zouzhihong@buaa.edu.cn.
authors.forename: Zhihong
authors.initials: Z
authors.lastname: Zou
authors.affiliation: School of Economics and Management, Beihang University, Beijing 100191, China.
authors.forename: Yanfei
authors.initials: Y
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D002681:China; D008956:Models, Chemical; D010100:Oxygen / Q000737:chemistry*; D062066:Water Resources / Q000032:analysis*
chemical_list: D010100:Oxygen
keywords: Dissolved oxygen; Initial condition; Nonlinear grey Bernoulli model; Particle swarm optimization; Water quality forecasting