Apriori association rule and K-means clustering algorithms for interpretation of pre-event landslide areas and landslide inventory mapping
| Autor: | Mehmet Özgür Çelik, Aydın Alptekin, Murat Yakar, Lütfiye Kuşak, Fatma Bünyan Ünel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
landslide
Apriori algorithm QE1-996.5 Association rule learning Computer science apriori algorithm uav landslide inventory k-means clustering k-means algorithm Landslide Geology Environmental Science (miscellaneous) computer.software_genre Interpretation (model theory) General Earth and Planetary Sciences A priori and a posteriori Data mining computer Event (probability theory) |
| Zdroj: | Open Geosciences, Vol 13, Iss 1, Pp 1226-1244 (2021) |
| ISSN: | 2391-5447 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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