With the development of marine economy, eutrophication has become one of the key issues in the marine environment. In this paper, a eutrophication model for Bohai Bay based on the cellular automata-support vector machine (CA-SVM) has been established by applying the soft computing approach with a large quantity of remote sensing data to the marine environment. In order to optimise the coupled model further, two main tasks have been done in this study. First, to choose reasonable influence factors as the input parameters of the model, nine series of training and simulation exercises were conducted based on nine different types of input parameter combinations. A reasonable input parameter combination was selected, and the eutrophication model (the basic model) was established by the comparative analysis of the simulation results. Second, according to Shelford's Law of Tolerance, an optimised model was developed. It is combined of nine special models and each model corresponds to a stage of sea surface temperature and the chlorophyll-a concentration, respectively. The comparison between the optimised model and the basic model indicated that prediction accuracy was improved by the optimised model. By this study, it can be observed this model could provide a scientific basis for the prediction and management of the aquatic environment of Bohai Bay.
Optimising the modelling of eutrophication for Bohai Bay based on the cellular automata – support vector machine method
Zheng Dongsheng, Xiang Xianquan, Tao Jianhua; Optimising the modelling of eutrophication for Bohai Bay based on the cellular automata – support vector machine method. Journal of Hydroinformatics 1 September 2014; 16 (5): 1125–1141. doi: https://doi.org/10.2166/hydro.2014.098
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