The Effect of Wind Speed in Discovery of Oil Spill by Polarimetric Radar

The oil spill into the oceans from oil tankers and oil pipelines has an ecological importance and a social and economic impact on coastal environments. Rapid detection of deliberate and accidental oil leaks can reduce serious risks to coastal residents and help pollutants be identified. The purpose of this research is to study the effects of thrust coefficient in wind under severe tidal conditions in the area of oil floods motion caused by oil tanker collision with a tower in Hebei typhoon in the western coastal zone of Korea. In order to separate oil from similar samples, Convolutional Neural Network (CNN) was used. The noise loss of the open data scattered by the Boxcar filter was modified and the motion of the oil flood was calculated by using a simple simulation model based on the experimental formula as the performance of surface water flow, wind speed and wind thrust factor. To simulate, the Environmental Fluid Dynamics Code (EFDC) and Automatic Weather Station (AWS) were used to generate tidal and wind fields. The simulated results were then compared with two samples of the data of the Synthetic Aperture Radar, Sentinel-1 and TerraSAR-X. From the present study, it is found that the highest match between the simulation results and the satellite images is obtained with different values of wind thrust and this factor is linearly proportionate to the wind speed.  According to the results, a new modified experimental formula is proposed to predict the flow of oil flood motion in the coastal zone.