High-resolution circulation forecasting of the Maenggol Channel, south coast of Korea

The Maenggol Channel and Uldolmok Strait, located on the south-west coast of Korea, have notably strong and complex currents due to tidal effects and to local geological factors. In these areas, electric power has been generated using strong tidal currents, the speed of which is more than 3 m/s during spring tides. The region also provides a shortcut for navigation. These tidal conditions are therefore sometimes useful, but may also cause terrible accidents or severe economic damage, in the absence of accurate information regarding ocean conditions. In April 2014, the passenger ferry MV Sewol capsized in the Maenggol Channel, with 295 passengers killed and 9 still missing. While this was unquestionably a man-made disaster, strong currents were one of the contributing causes. It was also difficult to conduct scuba diving rescue operations given strong current speeds,and accurate prediction of the time when the tide would turn was thus critically needed. In this research, we used the high-resolution coastal circulation forecasting system of KOOS（Korea Operational Oceanographic System） for analysis and simulation of strong tidal currents in such areas with many small islands, using measurements and modeling from this research area. For accurate prediction of tidal currents, small grid size-modeling was needed,and in this study, we identified a suitable grid size that offers efficiency as well as accuracy.

Modern data sources and techniques for analysis and forecast of road accidents:A review

Road accidents are one of the most relevant causes of injuries and death worldwide,and therefore,they constitute a significant field of research on the use of advanced algorithms and techniques to analyze and predict traffic accidents and determine the most relevant elements that contribute to road accidents.The research of road accident prediction aims to respond to the challenge of offer tools to generate a more secure mobility environment,and ultimately,save lives.This paper aims to provide an overview of the state of the art in the prediction of road accidents through machine learning algorithms and advanced techniques for analyzing information,such as convolutional neural networks and long short-term memory networks,among other deep learning architectures.Furthermore,in this article,a compendium and study of the most used data sources for the road accident forecast is made.And a classification is proposed according to its origin and characteristics,such as open data,measurement technologies,onboard equipment and social media data.For the analysis of the information,the different algorithms employed to make predictions about road accidents are listed and compared,as well as their applicability depending on the types of data being analyzed,along with the results obtained and their ease of interpretation and analysis.The best results reported by the authors are obtained when two or more analytic techniques are combined,in such a way that analysis of the obtained results is strengthened.Among the future challenges in road traffic forecasting lies the enhancement of the scope of the proposed models and predictions by the incorporation of heterogeneous data sources,that include geo spatial data,information from traffic volume,traffic statistics,video,sound,text and sentiment from social media,that many authors concur that can improve the precision and accuracy of the analysis and predictions.