JpGU Meeting 2019
Session outline| Atmospheric and Hydrospheric Sciences (A) | ||
|---|---|---|
| Session Sub-category | Complex & General(CG) | |
| Session ID | A-CG36 | |
| Title | Earth & Environmental Sciences and Artificial Intelligence | |
| Short Title | Earth & Environmental Sciences and AI | |
| Main Convener | Name | Tomohiko Tomita |
| Affiliation | Faculty of Advanced Science and Technology, Kumamoto University | |
| Co-Convener 1 | Name | Ken-ichi Fukui |
| Affiliation | Osaka University | |
| Co-Convener 2 | Name | Daisuke Matsuoka |
| Affiliation | Japan Agency for Marine-Earth Science and Technology | |
| Co-Convener 3 | Name | Satoshi Ono |
| Affiliation | Kagoshima University | |
| Session Language | J | |
| Scope | 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. | |
| Presentation Format | Oral and Poster presentation | |
| Invited Authors | Shinichi Shirakawa (Yokohama National University) | |
| Time | Presentation No | Title | Presenter | Abstract |
|---|---|---|---|---|
| Oral Presentation May 30 AM2 | ||||
| 10:50 - 11:20 | ACG36-01 | Recent Progress in Deep Neural Network Architectures and their Applications | Shinichi Shirakawa | |
| 11:20 - 11:35 | ACG36-02 | Improving performance of oxidant prediction using machine learning by optimizing input data based on atmospheric science knowledge | Tomohiro Sato | |
| 11:35 - 11:50 | ACG36-03 | Spatiotemporal Climate Forecasting with ConvLSTM | Ekasit Phermphoonphiphat | |
| 11:50 - 12:05 | ACG36-04 | Deep-Learning Based Short-Term Prediction Method for Sea Ice Concentration Using Explanatory Variables | Issei Kawashima | |
| Oral Presentation May 30 PM1 | ||||
| 13:45 - 14:00 | ACG36-05 | Remote sensing of cloud using the deep learning based on three-dimensional radiative transfer | Hironobu Iwabuchi | |
| 14:00 - 14:15 | ACG36-06 | Time series of cloud cover during the night derived from an omnidirectional camera | Karin miyabe | |
| 14:15 - 14:30 | ACG36-07 | Time series prediction of cloud cover using whole-sky images and meteorological elements | Kazunori Ogohara | |
| 14:30 - 14:45 | ACG36-08 | Fundamental investigation about the inductive prediction of precipitation based on the time series data | Naoki Matsumoto | |
| 14:45 - 15:00 | ACG36-09 | Identification of ships image on SAR data using machine learning | Takashi Sonoke | |
| Presentation No | Title | Presenter | Abstract |
|---|---|---|---|
| Poster Presentation May 30 PM2 | |||
| ACG36-P01 | Automatic Detection of Illegal Waste Sites in UAS Orthoimages | Yu-Ching Lin | |
| ACG36-P02 | Using machine learning models to predict the benefits of solar power generation in northern Taiwan | SHIH-CHIAO TSAI | |
| ACG36-P03 | Change Point Detection and Visualization of Region of Interests on Weather Time Series Data Using Three-dimensional Convolutional Neural Network | Satoshi Ono | |
| ACG36-P04 | Estimation of sediment disaster risk around the Fuji River Basin using a machine learning method that considers trigger and inherent factors | Kazuyoshi Souma | |
| ACG36-P05 | Application of deep learning techniques to precipitation guidance in short-term weather predictions | TAKERU KURAKAMI | |
| ACG36-P06 | Characteristics of precipitation in Japan evaluated from the ratio of precipitation events | Kenta Ogino | |