STUDY ON MONITORING EFFICIENCY OF ACTIVE VOLCANO BY DEEP LEARNING
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- YAMAWAKI Masashi
- 株式会社建設技術研究所 技術本部新技術推進部 AIソリューション室
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- UEYAMA Kou
- 株式会社建設技術研究所 東京本社情報部
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- NAKAMURA Naoto
- 株式会社建設技術研究所 東京本社情報部
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- KIKAWA Kenji
- 株式会社建設技術研究所 東京本社情報部
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- ISHIDA Kouji
- 国土交通省 北陸地方整備局 松本砂防事務所
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- TANIHO Kazunori
- 国土交通省 北陸地方整備局 松本砂防事務所
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- YOSHIZAKI Takayoshi
- 国土交通省 北陸地方整備局 富山河川国道事務所
Bibliographic Information
- Other Title
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- 深層学習による活火山監視効率化に関する研究
Abstract
<p> In Japan, there are 111 active volcanoes that account for about 7% of the world. Once a volcano erupts, devastating damage occurs due to eruption events such as volcanic cinders, pyroclastic flows and debris flows. Therefore, it is important to promptly detect signs of eruption and take countermeasures through regular observation and monitoring of active volcanoes.</p><p> In this study, we considered the method using deep learning of AI technology to improve the efficiency of active volcano monitoring. Specifically, by using CNN(Convolutional Neural Network) of the deep learning model, a model that removes noise such as clouds and fog that hinders volcano monitoring and a model that detects eruption events such as smoke of volcano and debris flows were constructed. The target volcano was Yakedake, one of the 50 active volcanoes that the Japan Meteorological Agency is constantly monitoring. As a result, it was shown that deep learning could be an effective technique for improving the efficiency of active volcano monitoring.</p>
Journal
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- Journal of Japan Society of Civil Engineers, Ser. F3 (Civil Engineering Informatics)
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Journal of Japan Society of Civil Engineers, Ser. F3 (Civil Engineering Informatics) 75 (2), I_22-I_29, 2019
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390002184892728064
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- NII Article ID
- 130007829052
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- ISSN
- 21856591
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed