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Geomatics, Natural Hazards and Risk Volume 12, 2021 - Issue 1

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Hazard mapping of ground subsidence in east area of Sapporo using frequency ratio model and GIS

Taeyoo Naa Graduate School of International Resource Sciences, Akita University, Akita, Japan

Youhei Kawamuraa Graduate School of International Resource Sciences, Akita University, Akita, Japan;b North China Institute of Science and Technology, Beijing, ChinaCorrespondence[email protected]

Seong-seung Kangc Department of Energy Resources Engineering, Chosun University, Gwangju, Republic of Korea

Shinji Utsukid Consulting company of Utsuki Geo Solution, Tokyo, Japan

Pages 347-362 | Received 24 Sep 2020, Accepted 05 Jan 2021, Published online: 28 Jan 2021

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https://doi.org/10.1080/19475705.2021.1873198
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Articles from other publishers (6)

Cheng-Yu Ku & Chih-Yu Liu. (2023) Modeling of land subsidence using GIS-based artificial neural network in Yunlin County, Taiwan. Scientific Reports 13:1.
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Chanita Duangyiwa & Pannee Cheewinsiriwat. 2023. Applied Geography and Geoinformatics for Sustainable Development. Applied Geography and Geoinformatics for Sustainable Development 1 17 .

Alberto Vavassori, Angelly De Jesús Pugliese Viloria & Maria Antonia Brovelli. (2022) Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia. GeoScape 16:2, pages 93-107.
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Mohsen Abbasi Sekkeravani, Ommolbanin Bazrafshan, Hamid Reza Pourghasemi & Arashk Holisaz. (2022) Spatial modeling of land subsidence using machine learning models and statistical methods. Environmental Science and Pollution Research 29:19, pages 28866-28883.
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Fatemeh Rezaie, Mahdi Panahi, Jongchun Lee, Jungsub Lee, Seonhong Kim, Juhee Yoo & Saro Lee. (2022) Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms. Environmental Pollution 292, pages 118385.
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A.C. Teodoro & L. Duarte. 2022. Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention. Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention 217 230 .

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