[A-CG43] Earth & Environmental Sciences and Artificial Intelligence/Machine Learning

Thu. Jun 3, 2021 3:30 PM - 5:00 PM Ch.06 (Zoom Room 06)

convener:Tomohiko Tomita(Faculty of Advanced Science and Technology, Kumamoto University), Shigeki Hosoda(Japan Marine-Earth Science and Technology), Ken-ichi Fukui(Osaka University), Satoshi Ono(Kagoshima Univeristy), Chairperson:Shigeki Hosoda(Japan Marine-Earth Science and Technology), Tomohiko Tomita(Faculty of Advanced Science and Technology, Kumamoto University)

In recent years, we have been required to explore various gigantic data leading earth and environmental sciences such as modern meteorology, oceanography, hydrology and so on to accomplish Society 5.0 and Sustainable Development Goals (SDGs). To examine such gigantic environmental data faithfully, it may be required to use the techniques of artificial intelligence/machine learning (AI/ML) including spatiotemporal data modeling of AI, prediction and detection by ML, techniques of automated data mining, and so forth. At the present, AI/ML is also expected to lead the data-driven science as a bridge connecting science and big data. This session calls for various studies applying the techniques of AI/ML to scientific/environmental issues and will discuss them in both natural and information sciences.

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3:30 PM - 3:50 PM

[ACG43-07] Automated identification of tornadic vortex with Doppler radar data using deep learning approaches: Recent progress and challenges

★Invited Papers

*Kusunoki Kenichi¹, Naoki Ishitsu², Toru Adachi¹, Hanako Inoue¹, Osamu Suzuki¹, Ken-ichiro Arai², Chusei Fujiwara³, Hiroto Suzuki³ (1.Meteorological Research Institute, 2.Alpha-denshi Co., Ltd., 3.East Japan Railway Company)

3:50 PM - 4:10 PM

[ACG43-08] Estimation of cloud and meteorological state from satellite image by deep neural network

★Invited Papers

*Hironobu Iwabuchi¹, Xinyue Wang¹, Hana Kato¹, Takaya Yamashita¹ (1.Graduate School of Science, Tohoku University)

4:10 PM - 4:25 PM

[ACG43-09] Clustering global ocean profiles according to temperature-salinity structure

*Nozomi Sugiura¹ (1.Japan Agency for Marine-Earth Science and Technology)

4:25 PM - 4:40 PM

[ACG43-10] Automated Hybrid Machine Learning Approaches for Groundwater Level Forecasting in Kumamoto area, Japan

*A T M Sakiur Rahman¹, Takahiro Hosono^4,5, John Quilty², Jayanta Das³, Amiya Basak³ (1.Postdoctoral researcher, Kumamoto University, 2.Department of Civil and Environmental Engineering , University of Waterloo, Waterloo, ON, Canada , 3.Department of Geography and Applied Geography, University of North Bengal, Darjeeling - 734013, India , 4.Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan, 5.International Research Organization for Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan)

4:40 PM - 4:55 PM

[ACG43-11] Improving Detection of Tropical Cyclones by Deep Convolutional Neural Network through a Two-step Training

*TAKERU TSUCHIYA¹, Shunji Kotsuki², Ryota Kikuchi³, Takeshi Umezawa⁴, Noritaka Osawa⁴ (1.Department of Information Engineering, Chiba University, Chiba, Japan, 2.Center for Environmental Remote Sensing, Chiba University, Chiba, Japan, 3.Office of Society Academia Collaboration for Innovation, Kyoto University, Japan, 4.Graduate School & Faculty of Engineering, Chiba University, Chiba, Japan)

4:55 PM - 5:00 PM

Session information

[A-CG43] Earth & Environmental Sciences and Artificial Intelligence/Machine Learning

[ACG43-07] Automated identification of tornadic vortex with Doppler radar data using deep learning approaches: Recent progress and challenges

★Invited Papers

[ACG43-08] Estimation of cloud and meteorological state from satellite image by deep neural network

★Invited Papers

[ACG43-09] Clustering global ocean profiles according to temperature-salinity structure

[ACG43-10] Automated Hybrid Machine Learning Approaches for Groundwater Level Forecasting in Kumamoto area, Japan

[ACG43-11] Improving Detection of Tropical Cyclones by Deep Convolutional Neural Network through a Two-step Training

[ACG43-12] Discussion

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