GIS-based Simulation and Visualization of oil spill spreading and SAR optimal planning

Title
GIS-based Simulation and Visualization of oil spill spreading and SAR optimal planning
Author(s)
김진아; 박광순
KIOST Author(s)
Kim, Jinah(김진아)
Publication Year
2016-07-04
Abstract
We developed a simulation and visualization tool based on ArcGIS for predicting spread and movement of oil spill and search and rescue (SAR) to prevent oil spill and ship accident in the ocean, respectively. It consists of 5 parts: oceanic and atmospheric forecast model, model for drift and leeway, initial condition (time and location of occurrence and object/oil type) and real-time particle trajectories visualization. It has been used very helpfully to support ocean-related accidents in Korea.Korea operational oceanographic system (KOOS) has been developed since 2009 and provided continuously a variety of observed and numerically simulated meteorological and oceanographic data. These data are essential to understand and predict the sea and applicable to fisheries, safe navigation, leisure activity, coastal development, and ocean-related problems such as marine accidents, marine pollution (ex. red tide), oil spill accidents, air crashed, shipwreck, and storm surge or tsunami inundation. To explore, analyze and extract information and knowledge among the raw data for coastal management and decision making, web-based oceanographic information system is implemented using ArcGIS techniques. The variables which are observed real-timely and predicted for 72-hours ahead about the sea states are sea surface wind, sea surface pressure, water elevation, surge, tides, currents, wave, sea temperature and salinity. Acquired dand atmospheric forecast model, model for drift and leeway, initial condition (time and location of occurrence and object/oil type) and real-time particle trajectories visualization. It has been used very helpfully to support ocean-related accidents in Korea.Korea operational oceanographic system (KOOS) has been developed since 2009 and provided continuously a variety of observed and numerically simulated meteorological and oceanographic data. These data are essential to understand and predict the sea and applicable to fisheries, safe navigation, leisure activity, coastal development, and ocean-related problems such as marine accidents, marine pollution (ex. red tide), oil spill accidents, air crashed, shipwreck, and storm surge or tsunami inundation. To explore, analyze and extract information and knowledge among the raw data for coastal management and decision making, web-based oceanographic information system is implemented using ArcGIS techniques. The variables which are observed real-timely and predicted for 72-hours ahead about the sea states are sea surface wind, sea surface pressure, water elevation, surge, tides, currents, wave, sea temperature and salinity. Acquired d
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/24659
Bibliographic Citation
ESRI User Conference 2016, pp.1, 2016
Publisher
ESRI
Type
Conference
Language
English
Publisher
ESRI
Related Researcher
Research Interests

AI/Machine Learning,Climate Change,Marine Disaster,인공지능/기계학습,기후변화,해양기상재해

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