Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

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. [Display omitted] •Fuzzy decision trees (FDTs) were optimized for selecting oil spill response in the Arctic.•Multi-objective metaheuristic optimization improved the training of FDTs.•Different regression analyses were coupled with FDTs to improve prediction accuracy.•Four information discrimination measures were evaluated to construct efficient FDTs.