[S-VC38] Active Volcanism

2:45 PM - 3:00 PM

[SVC38-22] Field survey report on Zao Volcano (2012−2018)

*Akio Goto¹, Noriyoshi Tsuchiya², Nobuo Hirano², Miwa Kuri³, Tetsuya Matsunaka⁴, Kengo Sawayama² (1.Center for Northeast Asian Studies, Tohoku University, 2.Graduate School of Environmental Studies, Tohoku University, 3.International Research Institute of Disaster Science, Tohoku University, 4.Institute of Nature and Environmental Technology, Kanazawa University)

Mon. May 27, 2019 1:45 PM - 3:15 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

[J] Poster
| M (Multidisciplinary and Interdisciplinary)
| M-GI General Geosciences, Information Geosciences & Simulations

[M-GI33] Data-driven geosciences

[MGI33-P03] Geochemical data processing using machine learning with artificial training data and proposal of environmental evaluation method considering natural background of heavy metals in river water in mining area

*Satoh Kenta¹, Kengo Sawayama¹, Noriyoshi Tsuchiya¹ (1.graduate school of environmental studies, Tohoku University)

Mon. May 27, 2019 5:15 PM - 6:30 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

[J] Poster
| M (Multidisciplinary and Interdisciplinary)
| M-GI General Geosciences, Information Geosciences & Simulations

[M-GI33] Data-driven geosciences

[MGI33-P03] Geochemical data processing using machine learning with artificial training data and proposal of environmental evaluation method considering natural background of heavy metals in river water in mining area

*Satoh Kenta¹, Kengo Sawayama¹, Noriyoshi Tsuchiya¹ (1.graduate school of environmental studies, Tohoku University)

Kengo Sawayama

Speaker, Author, Co-Author

[S-VC38] Active Volcanism

[SVC38-22] Field survey report on Zao Volcano (2012−2018)

[M-GI33] Data-driven geosciences

[MGI33-P03] Geochemical data processing using machine learning with artificial training data and proposal of environmental evaluation method considering natural background of heavy metals in river water in mining area

[M-GI33] Data-driven geosciences

[MGI33-P03] Geochemical data processing using machine learning with artificial training data and proposal of environmental evaluation method considering natural background of heavy metals in river water in mining area