LOW DATA REQUIREMENTS FRAMEWORK for LANDSLIDE SUSCEPTIBILITY PREDICTION USING 3D ALOS PALSAR IMAGES and NEURAL NETWORKS

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dc.contributor.authorAbujayyab, S.K.M.
dc.contributor.authorKaras, I.R.
dc.date.accessioned2024-09-29T16:16:05Z
dc.date.available2024-09-29T16:16:05Z
dc.date.issued2019
dc.departmentKarabük Üniversitesien_US
dc.description6th International Conference on Geomatics and Geospatial Technology, GGT 2019 -- 1 October 2019 through 3 October 2019 -- Kuala Lumpur -- 158822en_US
dc.description.abstractDevelopment of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) × 150 neurons (hidden layer) × 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53 km2 (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem. © 2019 S. K. M. Abujayyab.en_US
dc.description.sponsorshipTUBITAK; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAKen_US
dc.identifier.doi10.5194/isprs-archives-XLII-4-W16-47-2019
dc.identifier.endpage54en_US
dc.identifier.issn1682-1750
dc.identifier.issue4/W16en_US
dc.identifier.scopus2-s2.0-85083239904en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage47en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-archives-XLII-4-W16-47-2019
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8852
dc.identifier.volume42en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectALOS PALSARen_US
dc.subjectlandslide susceptibilityen_US
dc.subjectlow requirements framework.en_US
dc.subjectneural networksen_US
dc.subjecttopographic attributesen_US
dc.titleLOW DATA REQUIREMENTS FRAMEWORK for LANDSLIDE SUSCEPTIBILITY PREDICTION USING 3D ALOS PALSAR IMAGES and NEURAL NETWORKSen_US
dc.typeConference Objecten_US

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