Sensitive Resource and Traffic Density Risk Analysis of Marine Spill Accidents Using Automated Identification System Big Data

This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.

Optimal control of natural resources in mining industry

The paper focuses on the optimal control of natural resources in mining industry. The purpose is to pro- pose an optimal extraction series of these resources during the lifetime of the Mine＇s maintenance. Fol- lowing the proposed optimal control model, a sensitivity analysis has been performed that includes the interest rate impact on the optimal solution. This study shows that the increasing of the interest rate sti- mulates faster extraction of the resources. The discounting factor induces that the resource has to be extracted faster hut this effect is counterbalanced by the diminishing returns of the annual cash flow. At higher parameters of ＂alpha＂ close to one of the power function about 80% from the whole resource will be extracted during the first 4 years of object/mine maintenance. An existence of unique positive root with respect to return of investment has been proposed and proved by two ways： by the ＂method of chords＂ and by using specialized software.