Thu. May 30, 2019 1:45 PM - 3:15 PM
104 (1F)
convener:Tomohiko Tomita(Faculty of Advanced Science and Technology, Kumamoto University), Ken-ichi Fukui(Osaka University), Daisuke Matsuoka(Japan Agency for Marine-Earth Science and Technology), Satoshi Ono(Kagoshima University), Chairperson:Tomohiko Tomita(Kumamoto University, Faculty of Advanced Science and Technology)
In recent years, we have been required to explore the gigantic environmental data leading global environmental studies of modern meteorology, oceanography, and hydrology to accomplish Society 5.0 and Sustainable Development Goals (SDGs). Such environmental data, which are typical "big data," include the reliable long-term observatory data, ground radar data, satellite observation, oceanographic observation, global objective analysis data, and so on. However, it is hard to say that such big data are fully utilized, or nobody may have examined many of them. Therefore, to examine the global environment more faithfully, we need to apply the techniques of artificial intelligence/machine learning on such big data, that is, spatiotemporal data modeling of artificial intelligence, prediction and detection by machine learning, techniques of automated data mining, and so forth. Only cross-cutting diagnosis of the gigantic environmental data could resolve a wide variety of environmental problems including measures against global warming. In addition, the application of artificial intelligence/machine learning on the big data would raise us to the 4th paradigm of science (the 1st paradigm, observation/experiment; 2nd, theory; 3rd, simulation; 4th, data driven). This session sincerely invites various research initiatives that accelerate the application of artificial intelligence/machine learning on such gigantic environmental data.
