An Analytical Study on Soil Water Index (SWI), Landslide Prediction and Other Related Factors Using XRAIN Data during the July 2018 Heavy Rain Disasters in Hiroshima, Japan

IF 2.2 4区工程技术 Q3 ENGINEERING, GEOLOGICAL

Environmental geotechnics Pub Date : 2023-07-21 DOI:10.3390/geotechnics3030037

José Maria dos Santos Rodrigues Neto, N. Bhandary, Y. Fujita

{"title":"An Analytical Study on Soil Water Index (SWI), Landslide Prediction and Other Related Factors Using XRAIN Data during the July 2018 Heavy Rain Disasters in Hiroshima, Japan","authors":"José Maria dos Santos Rodrigues Neto, N. Bhandary, Y. Fujita","doi":"10.3390/geotechnics3030037","DOIUrl":null,"url":null,"abstract":"The rainfall-induced landslide disasters in July 2018 in Southwestern Japan yet again exemplified the severity of slope failure-related damage and the need for improvement of early warning systems. The Japanese Meteorological Agency (JMA) uses a method based on a threshold value of soil water index (SWI), a conceptual measurement that represents saturation of slope soil. The current SWI early warning system uses 60-min rainfall data on a 5-km2 mesh and does not take into consideration other landslide conditioning factors such as slope angle and geology. This study calculates SWI values during the July 2018 disasters in Kure City (Hiroshima Prefecture) using 1-min XRAIN rainfall data in a 250-m mesh to investigate the relationship between SWI and landslide occurrence. It was found that the SWI threshold of 124 mm used in the JMA early warning system for the area was surpassed in all cells. A new SWI threshold calculation method taking slope angle and geology into consideration and produced with machine learning is proposed, comprising power lines for different geological units at a two-dimensional graph where points located above the threshold line represent landslide risk. It is judged that this method would provide a more accurate early warning system for landslide disasters.","PeriodicalId":11823,"journal":{"name":"Environmental geotechnics","volume":"27 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental geotechnics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/geotechnics3030037","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}

引用次数: 0

Abstract

The rainfall-induced landslide disasters in July 2018 in Southwestern Japan yet again exemplified the severity of slope failure-related damage and the need for improvement of early warning systems. The Japanese Meteorological Agency (JMA) uses a method based on a threshold value of soil water index (SWI), a conceptual measurement that represents saturation of slope soil. The current SWI early warning system uses 60-min rainfall data on a 5-km2 mesh and does not take into consideration other landslide conditioning factors such as slope angle and geology. This study calculates SWI values during the July 2018 disasters in Kure City (Hiroshima Prefecture) using 1-min XRAIN rainfall data in a 250-m mesh to investigate the relationship between SWI and landslide occurrence. It was found that the SWI threshold of 124 mm used in the JMA early warning system for the area was surpassed in all cells. A new SWI threshold calculation method taking slope angle and geology into consideration and produced with machine learning is proposed, comprising power lines for different geological units at a two-dimensional graph where points located above the threshold line represent landslide risk. It is judged that this method would provide a more accurate early warning system for landslide disasters.

查看原文本刊更多论文

基于XRAIN数据的2018年7月日本广岛暴雨灾害土壤水分指数(SWI)、滑坡预测及相关因素分析研究

2018年7月在日本西南部发生的降雨引发的滑坡灾害再次证明了边坡破坏相关损害的严重性以及改进预警系统的必要性。日本气象厅(JMA)使用了一种基于土壤水指数(SWI)阈值的方法，这是一种代表斜坡土壤饱和度的概念性测量。目前的SWI预警系统采用的是5平方公里网格上60分钟的降雨数据，没有考虑坡角、地质等其他滑坡调节因素。本研究利用250米网1分钟XRAIN降雨数据，计算了2018年7月广岛县Kure市灾害期间的SWI值，探讨了SWI与滑坡发生的关系。结果发现，所有小区均超过了气象厅预警系统中使用的124 mm SWI阈值。提出了一种考虑坡角和地质因素并利用机器学习生成的新的SWI阈值计算方法，该方法在二维图中包含不同地质单元的电力线，位于阈值线上的点代表滑坡风险。该方法可为滑坡灾害提供更为准确的预警系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Environmental geotechnics Environmental Science-Water Science and Technology

CiteScore

6.20

自引率

18.20%

发文量

期刊介绍： In 21st century living, engineers and researchers need to deal with growing problems related to climate change, oil and water storage, handling, storage and disposal of toxic and hazardous wastes, remediation of contaminated sites, sustainable development and energy derived from the ground. Environmental Geotechnics aims to disseminate knowledge and provides a fresh perspective regarding the basic concepts, theory, techniques and field applicability of innovative testing and analysis methodologies and engineering practices in geoenvironmental engineering. The journal''s Editor in Chief is a Member of the Committee on Publication Ethics. All relevant papers are carefully considered, vetted by a distinguished team of international experts and rapidly published. Full research papers, short communications and comprehensive review articles are published under the following broad subject categories: geochemistry and geohydrology, soil and rock physics, biological processes in soil, soil-atmosphere interaction, electrical, electromagnetic and thermal characteristics of porous media, waste management, utilization of wastes, multiphase science, landslide wasting, soil and water conservation, sensor development and applications, the impact of climatic changes on geoenvironmental, geothermal/ground-source energy, carbon sequestration, oil and gas extraction techniques, uncertainty, reliability and risk, monitoring and forensic geotechnics.