[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-41959-en":3,"doc-seo-41959-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},41959,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Spatio-temporal Prediction and Mapping of Landslides Using MTInSAR-Learned Logistic Regression and Weight of Evidence Modeling in Urban Environments","Rapid urbanization in İstanbul (Türkiye), especially between the Büyükçekmece and Küçükçekmece lakes, increases exposure to landslide hazards driven by geological, geomorphological, tectonic, climatic, groundwater and anthropological factors. A spatio-temporal susceptibility study applies MTInSAR-learned logistic regression and weight of evidence modeling. MTInSAR processing of ALOS Palsar-1 SAR imagery first learns spatial and temporal landslide-motion change. LR and WofE then estimate susceptibility, validated with ROC/AUC, statistical metrics and spatial proportions, while assessing predictor-variable effects. Results indicate strong forecasting and good fit in urban, landslide-prone areas.","Environmental Earth Sciences (2023) 82:390  \n[https://doi.org/10.1007/s12665-023-1](https://doi.org/10.1007/s12665-023-1)1064-1  \nSpatio‑temporal prediction and mapping of landslides  \nusing MTInSAR‑learning logistic regression and weight of evidence modeling in urban environments: a case study for the Büyükçekmece–Küçükçekmece region, İstanbul, Türkiye  \nÖnder Kayadibi1  \nReceived: 8 November 2022 / Accepted: 15 July 2023 / Published online: 3 August 2023  \n© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023  \nAbstract  \nThe megacity İstanbul (Türkiye) is rapidly urbanizing with a high population growth. Similar to this, the region of İstanbul Metropole between the Büyükçekmece and Küçükçekmece lakes is being built up to a large extent. Landslides in this region, however, cause major socio-economic damage due to geological, geomorphological, tectonic, climatic, groundwater, and anthropological factors. The aim of the study is to carry out a spatio-temporal susceptibility modeling of landslides using the multi-temporal InSAR (MTInSAR)-learned logistic regression (LR) and weight of evidence (WofE) methods. First, spatial and temporal change information in landslide motions was learned using MTInSAR analysis of ALOS Palsar-1 SAR pictures for this purpose. Then, LR and WofE susceptibility modeling were performed using the previously learned information. The accuracy, model fit, and prediction power of susceptibility modeling and assessment findings were compared and evaluated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), statistical metrics, and spatial percentage proportions. Furthermore, the effects of geospatial predictor variables on the occurrence of landslides were investigated. The obtained findings demonstrated that the applied MTInSAR-based susceptibility modeling approach has a good forecast capability of landslide probability. Landslide susceptibility maps have a high degree of goodness-of-fit at the MTInSAR-learned training dataset and landslide-based validation dataset. The AUC, statistical metrics, and spatial percentage proportions have demonstrated the usefulness and effectiveness of the suggested technique in urban contextsand landslide-prone locations.  \nKeywords Landslide susceptibility · Multi-temporal InSAR · Logistic regression · Weight of evidence · Büyükçekmece– Küçükçekmece (İstanbul, Türkiye)  \nIntroduction  \nLandslides are one of the most devastating natural disasters that cause considerable socio-economic damage, along with floods, storms, earthquakes, volcanic activity, droughts, wildfires, and excessive temperatures, all of which cause significant loss of life and property. Every year, landslides cause significant damage to buildings, engineering structures, the environment, and agricultural areas, as well as  \n* Önder Kayadibi [okayadibi@gmail.com](okayadibi@gmail.com)  \n1 Department of Geological Research, Mineral Research and Exploration General Directorate, Remote Sensing and GIS Center, Ankara, Turkey  \nhuman deaths. Although 254 landslide events were reported in the world between 1980 and 1999, 376 landslide events were documented between 2000 and 2019. Landslides were accountable for 5% of disasters, ranking fifth behind floods, storms, earthquakes, and severe temperatures (CRED and UNISDR 2020) . Landslides damaged around 4.8 million people between 1998 and 2017, killing 18,414 people (Wallemacq and House 2018) .  \nLandslide movements are complex geological and geomorphological phenomena involving numerous causes. Landslide susceptibility modeling (LSM) predicts the spatial distribution of places where landslides may occur or are prone to landslides. It is one of the most important and effective methods for preventing landslide damage and reducing risks and hazards. They also contribute to the selection of  \nappropriate areas for land use, future planning of places, and urban growth.  \nMany m","cbCaiqTpmTtjFkBC","https://ap.wps.com/l/cbCaiqTpmTtjFkBC","pdf",7324380,6,1,22,"English","en",105,"# Abstract\n## Study objective and setting\n## MTInSAR learning and modeling workflow\n## Model evaluation and predictor analysis\n# Introduction\n## Disaster impact and frequency context\n## Landslide susceptibility modeling purpose\n## Modeling approaches and method categories","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To build spatio-temporal landslide susceptibility models for an urban area using MTInSAR-learned logistic regression and weight of evidence methods, incorporating spatial and temporal change information from satellite data.\"},{\"question\":\"How are MTInSAR data used in the modeling workflow?\",\"answer\":\"MTInSAR analysis of ALOS Palsar-1 SAR images is used first to learn spatial and temporal change information in landslide motions; this learned information is then used as input for logistic regression and weight of evidence susceptibility modeling.\"},{\"question\":\"How is landslide susceptibility model performance evaluated?\",\"answer\":\"Model accuracy and predictive power are assessed using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC), along with statistical metrics and spatial percentage proportion measures.\"}]",1783339999,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"spatio-temporal-prediction-and-mapping-of-landslides-using-mtinsar-learned-logistic-regression-and-weight-of-evidence-modeling-in-urban-environments","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/spatio-temporal-prediction-and-mapping-of-landslides-using-mtinsar-learned-logistic-regression-and-weight-of-evidence-modeling-in-urban-environments/41959/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-20","2026-07-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of the study?","Question",{"text":76,"@type":77},"To build spatio-temporal landslide susceptibility models for an urban area using MTInSAR-learned logistic regression and weight of evidence methods, incorporating spatial and temporal change information from satellite data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are MTInSAR data used in the modeling workflow?",{"text":81,"@type":77},"MTInSAR analysis of ALOS Palsar-1 SAR images is used first to learn spatial and temporal change information in landslide motions; this learned information is then used as input for logistic regression and weight of evidence susceptibility modeling.",{"name":83,"@type":74,"acceptedAnswer":84},"How is landslide susceptibility model performance evaluated?",{"text":85,"@type":77},"Model accuracy and predictive power are assessed using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC), along with statistical metrics and spatial percentage proportion measures.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]