Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping

In this paper, an inventory of the landslide that occurred in Karahacılı at the end of 2019 was created and the pre-landslide conditions of the region were evaluated with traditional statistical and spatial data mining methods. The current orthophoto of the region was created by unmanned aerial vehi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kusak Lutfiye, Unel Fatma Bunyan, Alptekin Aydın, Celik Mehmet Ozgur, Yakar Murat
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	apriori algorithm k-means algorithm landslide inventory landslide uav Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/498a4e4ec8df4a96b94de25415325b12
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:498a4e4ec8df4a96b94de25415325b12
record_format	dspace
spelling	oai:doaj.org-article:498a4e4ec8df4a96b94de25415325b122021-12-05T14:10:49ZApriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping2391-544710.1515/geo-2020-0299https://doaj.org/article/498a4e4ec8df4a96b94de25415325b122021-10-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0299https://doaj.org/toc/2391-5447In this paper, an inventory of the landslide that occurred in Karahacılı at the end of 2019 was created and the pre-landslide conditions of the region were evaluated with traditional statistical and spatial data mining methods. The current orthophoto of the region was created by unmanned aerial vehicle (UAV). In this way, the landslide areas in the region were easily determined. According to this, it was determined that the areas affected by the landslides had an average slide of 26.56 m horizontally. The relationships among the topographic, hydrographic, and vegetative factors of the region were revealed using the Apriori algorithm. It was determined that the areas with low vegetation in the study area with 55% confidence were of a Strong Slope feature from the Apriori algorithm. In addition, the cluster distributions formed by these factors were determined by K-means. Among the five clusters created with K-means, it was determined that the study area was 38% in the southeast, had a Strong Slope, Low Vegetation, Non-Stream Line, and a slope less than 140 m. K-means results of the study were made with performance metrics. Average accuracy, recall, specificity, precision, and F-1 score were found as 0.77, 0.69, 0.84, and 0.73 respectively.Kusak LutfiyeUnel Fatma BunyanAlptekin AydınCelik Mehmet OzgurYakar MuratDe Gruyterarticleapriori algorithmk-means algorithmlandslide inventorylandslideuavGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 1226-1244 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	apriori algorithm k-means algorithm landslide inventory landslide uav Geology QE1-996.5
spellingShingle	apriori algorithm k-means algorithm landslide inventory landslide uav Geology QE1-996.5 Kusak Lutfiye Unel Fatma Bunyan Alptekin Aydın Celik Mehmet Ozgur Yakar Murat Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
description	In this paper, an inventory of the landslide that occurred in Karahacılı at the end of 2019 was created and the pre-landslide conditions of the region were evaluated with traditional statistical and spatial data mining methods. The current orthophoto of the region was created by unmanned aerial vehicle (UAV). In this way, the landslide areas in the region were easily determined. According to this, it was determined that the areas affected by the landslides had an average slide of 26.56 m horizontally. The relationships among the topographic, hydrographic, and vegetative factors of the region were revealed using the Apriori algorithm. It was determined that the areas with low vegetation in the study area with 55% confidence were of a Strong Slope feature from the Apriori algorithm. In addition, the cluster distributions formed by these factors were determined by K-means. Among the five clusters created with K-means, it was determined that the study area was 38% in the southeast, had a Strong Slope, Low Vegetation, Non-Stream Line, and a slope less than 140 m. K-means results of the study were made with performance metrics. Average accuracy, recall, specificity, precision, and F-1 score were found as 0.77, 0.69, 0.84, and 0.73 respectively.
format	article
author	Kusak Lutfiye Unel Fatma Bunyan Alptekin Aydın Celik Mehmet Ozgur Yakar Murat
author_facet	Kusak Lutfiye Unel Fatma Bunyan Alptekin Aydın Celik Mehmet Ozgur Yakar Murat
author_sort	Kusak Lutfiye
title	Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
title_short	Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
title_full	Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
title_fullStr	Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
title_full_unstemmed	Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
title_sort	apriori association rule and k-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/498a4e4ec8df4a96b94de25415325b12
work_keys_str_mv	AT kusaklutfiye aprioriassociationruleandkmeansclusteringalgorithmsforinterpretationofpreeventlandslideareasandlandslideinventorymapping AT unelfatmabunyan aprioriassociationruleandkmeansclusteringalgorithmsforinterpretationofpreeventlandslideareasandlandslideinventorymapping AT alptekinaydın aprioriassociationruleandkmeansclusteringalgorithmsforinterpretationofpreeventlandslideareasandlandslideinventorymapping AT celikmehmetozgur aprioriassociationruleandkmeansclusteringalgorithmsforinterpretationofpreeventlandslideareasandlandslideinventorymapping AT yakarmurat aprioriassociationruleandkmeansclusteringalgorithmsforinterpretationofpreeventlandslideareasandlandslideinventorymapping
_version_	1718371719061700608

Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping

Ejemplares similares