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

Mohammadiun, S. and Hu, G. and Alavi Gharahbagh, A. and Mirshahi, R. and Li, J. and Hewage, K. and Sadiq, R. (2021) Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic. Knowledge-Based Systems, 213.

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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. © 2020 Elsevier B.V.

Item Type: Article
Additional Information: cited By 0
Uncontrolled Keywords: Decision support systems; Decision trees; Evolutionary algorithms; Forecasting; Oil field development; Oil spills; Optimization; Regression analysis, Decision support tools; Differential Evolution; Gaussian process regression; Information evaluation; Misclassification error; Non-dominated Sorting; Prediction performance; Predictive performance, Predictive analytics
Subjects: QT Physiology
Depositing User: eprints admin
Date Deposited: 14 Mar 2021 10:22
Last Modified: 14 Mar 2021 10:22
URI: http://eprints.iums.ac.ir/id/eprint/32925

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