Inferring spatial distribution of oil spill risks from proxies: Case study in the north of the Persian Gulf

摘要

Maps of oil spill probability are important tools in environmental risk assessment and decision making in coastal zone management. This paper describes the development of a spatial predictive model for the probability of oil spills in the northern part of the Persian Gulf. The model estimates the probability of oil spills at a pixel level as function of four proxies, i.e., ship routes, coastlines, oil facilities, oil wells. It uses a generalized linear model (GLM) with a polynomial function that is implemented in the R software environment. For training the model, we used reported oil spill events that represent the location of their occurrences. We trained and tested the model in 100 iterations, using a different subset of data for each. The model evaluations showed mean accuracy of 0.79% (range 0.68%-0.89%), expressed by the area under the curve (AUC). In the northern part of the Persian Gulf, the largest probability of oil spills was predicted in areas where actual oil facilities in combination with high intensity ship traffic are in evidence. The model can predict the probability of oil spills as raster map in a standard R data format. It can be used in environmental risk assessment as well as an input for more detailed oil spill simulation models. The advantages of the model include its' high spatial resolution, accounting for uncertainty in oil spill locations, and the possibility of sharing as an open-source R script with other users. (C) 2015 Elsevier Ltd. All rights reserved.