Hazard mapping of ground subsidence in east area of Sapporo using frequency ratio model and GIS-Reference-Cited by-同舟云学术

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

Published:2021-01-01 Issue:1 Volume:12 Page:347-362
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Na Taeyoo¹,Kawamura Youhei¹²,Kang Seong-seung³,Utsuki Shinji⁴

Affiliation:

1. Graduate School of International Resource Sciences, Akita University, Akita, Japan

2. North China Institute of Science and Technology, Beijing, China

3. Department of Energy Resources Engineering, Chosun University, Gwangju, Republic of Korea

4. Consulting company of Utsuki Geo Solution, Tokyo, Japan

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2021.1873198

Reference23 articles.

1. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions

2. Landslide Susceptibility Mapping using Relative Frequency and Predictor Rate along Araniko Highway

3. Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic

4. Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea

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1. Comprehensive database of land subsidence in 143 major coastal cities around the world: overview of issues, causes, and future challenges;Frontiers in Earth Science;2024-08-27

2. Monitoring and Cause Analysis of Land Subsidence along the Yangtze River Utilizing Time-Series InSAR;ISPRS International Journal of Geo-Information;2024-07-01

3. Using neural network modeling to improve the detection accuracy of land subsidence due to groundwater withdrawal;Journal of Mountain Science;2024-07

4. Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms;International Journal of Coal Geology;2023-09

5. Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China;ISPRS International Journal of Geo-Information;2023-06-07

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