Landslides Risk Prediction Using Cascade Neural Networks Model at Mus In Turkey

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dc.authoridSaleh, Azlan/0000-0002-7457-3114
dc.authoridK. M. Abujayyab, Sohaib/0000-0002-6692-3567
dc.contributor.authorAbujayyab, Sohaib K. M.
dc.contributor.authorSaleh, Azlan
dc.date.accessioned2024-09-29T16:03:03Z
dc.date.available2024-09-29T16:03:03Z
dc.date.issued2020
dc.departmentKarabük Üniversitesien_US
dc.description10th Institution-of-Geospatial-and-Remote-Sensing-Malaysia(IGRSM) International Conference and Exhibition on Geospatial and Remote Sensing (IGRSM) -- OCT 20-21, 2020 -- ELECTR NETWORKen_US
dc.description.abstractGlobally, landslides risk represent a challenging issue that negatively affecting the infrastructure and human and neutral life. Among the former studies, the methods of predicting landslides risk maps found to be need further experiments. The aim of this study was to predict the landslide risk map at Mus in Turkey using cascade neural networks model. In this article, Trainlm function used to train 9954 sample points in the dataset using Matlab software. ArcGIS employed to prepare the explanatory variables, inventory landslides map, data sampling and producing the final landslides risk map. The developed model achieved the best performance accuracy by implanting an optimizer for the used number of neurons. After 60 training experiments, 52 neurons found the best number in this model. Chunks computing using Python programing in ArcGIS implemented to solve the intensive computing and data restructuring issues. Although the implementation at regional scale with 14015 km2, the final landslides risk map was successfully produced. The best-achieved performance accuracy was 80% based on receiver operating characteristic curve (ROC) and area under the curve (AUC). To summarize, the cascade neural networks model can reliably be implement in predicting landslides risk maps at regional-scale with the aid of chunks computing.en_US
dc.description.sponsorshipInst Geospatial & Remote Sensing Malaysia,Univ Putra Malaysia,Sc & Technol Res Inst Def,Univ Teknologi Malaysia,Univ Utara Malaysia,Int Islam Univ Malaysia,Forest Res Inst Malaysia,GIS Innovat Sdn Bhd,GPS Lands M Sdn Bhd,IEEE Geoscience & Remote Sensing Soc, Malaysia Chapteren_US
dc.identifier.doi10.1088/1755-1315/540/1/012081
dc.identifier.issn1755-1307
dc.identifier.scopus2-s2.0-85090139228en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1088/1755-1315/540/1/012081
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5866
dc.identifier.volume540en_US
dc.identifier.wosWOS:000617132600081en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIop Publishing Ltden_US
dc.relation.ispartof10th Igrsm International Conference and Exhibition On Geospatial & Remote Sensingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLogistic-Regressionen_US
dc.subjectFrequency Ratioen_US
dc.subjectSusceptibilityen_US
dc.titleLandslides Risk Prediction Using Cascade Neural Networks Model at Mus In Turkeyen_US
dc.typeConference Objecten_US

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