期刊
KNOWLEDGE-BASED SYSTEMS
卷 213, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106676
关键词
Oil spill response; Decision-making tool; Fuzzy decision tree; Multi-objective optimization; Regression analysis; Information discrimination power
资金
- Multi-Partner Oil Spill Research Initiative (MPRI) of Fisheries and Oceans Canada
This study developed a framework utilizing various integrated fuzzy decision tree and regression models to select appropriate response methods for oil spill accidents in Arctic waters, and improve predictive performance through model optimization. The prediction accuracy and the number of rules of FDTR models were increased, leading to a set of optimal prediction models for promptly selecting an appropriate response method. Among all models, GPR-GINI performed the best in terms of optimal values of objective functions.
The challenging oil spill response in the Arctic calls for effective response decision support tools. In this study, a framework comprising the development of various integrated fuzzy decision tree and regression (FDTR) models as well as model optimization was developed to facilitate the selection of suitable response methods for oil spill accidents in Arctic waters. The FDTR models took into account the influential attributes affecting the effectiveness of oil spill response in harsh Arctic environments. Different FDTR models were developed based on the combinations of three regression analyses, including linear, non-linear, and Gaussian process regression (GPR) and four information evaluation measures for splitting a decision tree, including information gain, deviance, GINI impurities (GINI), and misclassification error. Non-dominated sorting differential evolution (NSDE) optimization was employed to enhance the predictive performance of the FDTR models. The prediction performance of the FDTR models was compared using an oil spill dataset. Using this framework, the average prediction accuracy and the number of rules (representing the robustness) of FDTRs were increased by 14% and decreased by 57%, respectively. A set of optimal prediction models to promptly select an appropriate response method can be obtained using this framework. Among all models, GPR-GINI performed the best concerning optimal values of objective functions. (C) 2020 Elsevier B.V. All rights reserved.
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