{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360286996887656320.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1186/s40677-019-0137-5"}},{"identifier":{"@type":"URI","@value":"http://link.springer.com/content/pdf/10.1186/s40677-019-0137-5.pdf"}},{"identifier":{"@type":"URI","@value":"http://link.springer.com/article/10.1186/s40677-019-0137-5/fulltext.html"}}],"resourceType":"学術雑誌論文(journal article)","dc:title":[{"@value":"The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Abstract</jats:title><jats:sec>\n                <jats:title>Background</jats:title>\n                <jats:p>Thousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes.</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Results</jats:title>\n                <jats:p>By applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance.</jats:p>\n              </jats:sec><jats:sec>\n                <jats:title>Conclusions</jats:title>\n                <jats:p>The 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.</jats:p>\n              </jats:sec>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380286996887655943","@type":"Researcher","foaf:name":[{"@value":"Kounghoon Nam"}]},{"@id":"https://cir.nii.ac.jp/crid/1420564276181593216","@type":"Researcher","personIdentifier":[{"@type":"KAKEN_RESEARCHERS","@value":"10324097"},{"@type":"NRID","@value":"1000010324097"},{"@type":"NRID","@value":"9000016701512"},{"@type":"NRID","@value":"9000007021616"},{"@type":"NRID","@value":"9000415082637"},{"@type":"NRID","@value":"9000257957917"},{"@type":"NRID","@value":"9000283417978"},{"@type":"NRID","@value":"9000241816425"},{"@type":"NRID","@value":"9000007021764"},{"@type":"NRID","@value":"9000004148694"},{"@type":"NRID","@value":"9000023300432"},{"@type":"NRID","@value":"9000014589936"},{"@type":"RESEARCHMAP","@value":"https://researchmap.jp/read0065788"}],"foaf:name":[{"@value":"Fawu Wang"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"21978670"}],"prism:publicationName":[{"@value":"Geoenvironmental Disasters"}],"dc:publisher":[{"@value":"Springer Science and Business Media LLC"}],"prism:publicationDate":"2019-12","prism:volume":"6","prism:number":"1"},"reviewed":"false","dcterms:accessRights":"http://purl.org/coar/access_right/c_abf2","dc:rights":["https://creativecommons.org/licenses/by/4.0","https://creativecommons.org/licenses/by/4.0"],"url":[{"@id":"http://link.springer.com/content/pdf/10.1186/s40677-019-0137-5.pdf"},{"@id":"http://link.springer.com/article/10.1186/s40677-019-0137-5/fulltext.html"}],"createdAt":"2019-12-11","modifiedAt":"2020-12-10","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=Earthquake","dc:title":"Earthquake"},{"@id":"https://cir.nii.ac.jp/all?q=Autoencoder","dc:title":"Autoencoder"},{"@id":"https://cir.nii.ac.jp/all?q=Deep%20neural%20network","dc:title":"Deep neural network"},{"@id":"https://cir.nii.ac.jp/all?q=Landslide%20susceptibility","dc:title":"Landslide susceptibility"},{"@id":"https://cir.nii.ac.jp/all?q=Unsupervised%20learning","dc:title":"Unsupervised learning"},{"@id":"https://cir.nii.ac.jp/all?q=Disasters%20and%20engineering","dc:title":"Disasters and engineering"},{"@id":"https://cir.nii.ac.jp/all?q=Environmental%20sciences","dc:title":"Environmental sciences"},{"@id":"https://cir.nii.ac.jp/all?q=TA495","dc:title":"TA495"},{"@id":"https://cir.nii.ac.jp/all?q=GE1-350","dc:title":"GE1-350"}],"project":[{"@id":"https://cir.nii.ac.jp/crid/1040000782014034304","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"19H01980"},{"@type":"JGN","@value":"JP19H01980"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-19H01980/"}],"notation":[{"@language":"ja","@value":"降下火砕物斜面地域における降雨・地震による長距離運動地すべりの発生・運動機構"},{"@language":"en","@value":"Occurrence and motion mechanism of long runout landslide due to rainfall and earthquake in tephra deposit slope"}]}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050282813898655360","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya"}]},{"@id":"https://cir.nii.ac.jp/crid/1360011145206865280","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide mapping from multi-sensor data through improved change detection-based Markov random field"}]},{"@id":"https://cir.nii.ac.jp/crid/1360011145558862976","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows"}]},{"@id":"https://cir.nii.ac.jp/crid/1360292619096205440","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Reducing the Dimensionality of Data with Neural Networks"}]},{"@id":"https://cir.nii.ac.jp/crid/1360292619297016832","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy"}]},{"@id":"https://cir.nii.ac.jp/crid/1360292620100871808","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1360574093913782144","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Probabilistic landslide susceptibility and factor effect analysis"}]},{"@id":"https://cir.nii.ac.jp/crid/1360574094363870976","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models"}]},{"@id":"https://cir.nii.ac.jp/crid/1360574096516190336","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Method for Meteorological Early Warning of Precipitation-Induced Landslides Based on Deep Neural Network"}]},{"@id":"https://cir.nii.ac.jp/crid/1360846640349515648","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide susceptibility mapping of the Sera River Basin using logistic regression model"}]},{"@id":"https://cir.nii.ac.jp/crid/1360846643941801344","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia"}]},{"@id":"https://cir.nii.ac.jp/crid/1360855569054228480","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses"}]},{"@id":"https://cir.nii.ac.jp/crid/1360855569921155328","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data"}]},{"@id":"https://cir.nii.ac.jp/crid/1360855569940768256","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1360855570725462144","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models"}]},{"@id":"https://cir.nii.ac.jp/crid/1361137044492020608","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression"}]},{"@id":"https://cir.nii.ac.jp/crid/1361137045315656192","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Time series analysis and long short-term memory neural network to predict landslide displacement"}]},{"@id":"https://cir.nii.ac.jp/crid/1361137045447241728","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia"}]},{"@id":"https://cir.nii.ac.jp/crid/1361418518541918720","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Support vector machine modeling of earthquake-induced landslides susceptibility in central part of Sichuan province, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1361418520222301696","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1361694365785997184","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Characteristics of landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake, Northern Japan"}]},{"@id":"https://cir.nii.ac.jp/crid/1361699993652820608","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine"}]},{"@id":"https://cir.nii.ac.jp/crid/1361699994468946816","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines"}]},{"@id":"https://cir.nii.ac.jp/crid/1361699994602328704","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Artificial neural networks and cluster analysis in landslide susceptibility zonation"}]},{"@id":"https://cir.nii.ac.jp/crid/1361981468608874752","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan"}]},{"@id":"https://cir.nii.ac.jp/crid/1361981468842723584","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility"}]},{"@id":"https://cir.nii.ac.jp/crid/1361981469165405824","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1362262943437096960","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Front Cover"}]},{"@id":"https://cir.nii.ac.jp/crid/1362262943600323968","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping"}]},{"@id":"https://cir.nii.ac.jp/crid/1362262944213267840","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Formation and deformation processes of late Paleogene sedimentary basins in southern central Hokkaido, Japan: Paleomagnetic and numerical modeling approach"}]},{"@id":"https://cir.nii.ac.jp/crid/1362262945642205824","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Unsupervised Deep Feature Extraction for Remote Sensing Image Classification"}]},{"@id":"https://cir.nii.ac.jp/crid/1362544419070023936","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Disaster Area Detection from Synthetic Aperture Radar Images Using Convolutional Autoencoder and One-class SVM"}]},{"@id":"https://cir.nii.ac.jp/crid/1362544420106402176","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping"}]},{"@id":"https://cir.nii.ac.jp/crid/1362544420191136640","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size"}]},{"@id":"https://cir.nii.ac.jp/crid/1362825893682657152","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1362825895017209728","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1363107369205441024","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Understanding autoencoders with information theoretic concepts"}]},{"@id":"https://cir.nii.ac.jp/crid/1363107369285830016","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide susceptibility mapping in Injae, Korea, using a decision tree"}]},{"@id":"https://cir.nii.ac.jp/crid/1363107369680432640","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India"}]},{"@id":"https://cir.nii.ac.jp/crid/1363107370449400448","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"The latest Cretaceous‐Early Paleogene rapid growth of accretionary complex and exhumation of high pressure series metamorphic rocks in northwestern Pacific margin"}]},{"@id":"https://cir.nii.ac.jp/crid/1363107371080676224","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Data Mining of Inputs: Analysing Magnitude and Functional Measures"}]},{"@id":"https://cir.nii.ac.jp/crid/1363388843452584448","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison"}]},{"@id":"https://cir.nii.ac.jp/crid/1363388843877771264","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan"}]},{"@id":"https://cir.nii.ac.jp/crid/1363388844406712192","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment"}]},{"@id":"https://cir.nii.ac.jp/crid/1363388844516450816","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey)"}]},{"@id":"https://cir.nii.ac.jp/crid/1363388845357032320","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping"}]},{"@id":"https://cir.nii.ac.jp/crid/1363670318676864768","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China"}]},{"@id":"https://cir.nii.ac.jp/crid/1363670318799086208","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations"}]},{"@id":"https://cir.nii.ac.jp/crid/1363951794115240960","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Deep learning"}]},{"@id":"https://cir.nii.ac.jp/crid/1363951794285212288","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques"}]},{"@id":"https://cir.nii.ac.jp/crid/1363951795080427776","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Logistic Regression in Rare Events Data"}]},{"@id":"https://cir.nii.ac.jp/crid/1363951795388713984","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"On the influence of temporal change on the validity of landslide susceptibility maps"}]},{"@id":"https://cir.nii.ac.jp/crid/1364233269004353920","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Landslide Deformation Prediction Based on Recurrent Neural Network"}]},{"@id":"https://cir.nii.ac.jp/crid/1364233269253825280","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Three-dimensional seismic slope stability assessment with the application of Scoops3D and GIS: a case study in Atsuma, Hokkaido"}]},{"@id":"https://cir.nii.ac.jp/crid/1364233270920823552","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)"}]},{"@id":"https://cir.nii.ac.jp/crid/1364233271169832832","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey)"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282681217032320","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Interior structure and sliding process of landslide body composed of stratified pyroclastic fall deposits at the Apporo 1 archaeological site, southeastern margin of the Ishikari Lowland, Hokkaido, North Japan"},{"@language":"ja","@value":"成層した降下火砕堆積物からなる地すべり移動体の内部構造と形成過程："},{"@value":"成層した降下火砕堆積物からなる地すべり移動体の内部構造と形成過程 : 石狩低地東縁,厚幌1遺跡の例"},{"@language":"ja-Kana","@value":"セイソウ シタ コウシタビサイタイセキブツ カラ ナル ジスベリ イドウタイ ノ ナイブ コウゾウ ト ケイセイ カテイ : イシカリ テイチ トウエン,コウ ホロ 1 イセキ ノ レイ"}]},{"@id":"https://cir.nii.ac.jp/crid/2051433317021974016","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Detection and interpretation of local surface deformation from the 2018 Hokkaido Eastern Iburi Earthquake using ALOS-2 SAR data"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1186/s40677-019-0137-5"},{"@type":"KAKEN","@value":"PRODUCT-22968064"},{"@type":"OPENAIRE","@value":"doi_dedup___::556ee62c05c6dfecdcdc7b1d9d964110"}]}