Sığ Sinir Ağları Modeli Yardımıyla Türkiye’de Refahiye İlçesinin Heyelan Duyarlılığının Haritalanması

Yıl 2020, Cilt: 1 Sayı: 2, 61 - 77, 30.09.2020

Sohaib K M Abujayyab , İsmail Rakıp Karaş

Öz

Heyelanlar nüfus ve altyapı için sürekli bir tehlike oluşturmaktadır. Heyelan duyarlılığının haritalanması heyelan risklerini önlemek için önemli bir konudur. Bu çalışmanın amacı, Türkiye'nin Refahiye ilçesinde heyelan duyarlılık haritalaması için yüksek doğruluklu model üretmektir. Modelde heyelan duyarlılık haritalaması için sığ sinir ağlarını kullanılırken, uygun veriden gerekli faktörleri çıkarmak ve haritalama ve eğitim hesaplama süresini azaltmak için iki değişkenli Spearman sıra korelasyon testi kullanılmıştır. 21 mekansal faktörden 12'si, Spearman korelasyon testi kullanılarak ilgili faktörler olarak seçilmiştir. İlgili faktörler jeoloji, yollara uzaklık, jeolojik faylara olan uzaklık, su yollarına olan uzaklık, akış yönü, bakı, arazi kabartı, ısı yük endeksi, eğim / bakı dönüşümü, alan maruziyet indeksi, bileşik topografik indeks ve yüksekliktir. Oluşturulan veri kümesi, 10 katlı çapraz geçerlilik yöntemini kullanarak eğitim, doğrulama ve test veri kümelerine bölünmüştür. %86,3'lük genel doğruluk performansı elde edilen en iyi eğitim fonksiyonu (Trainlm)'dir. Geliştirilen NN modeli, IRIS kıyaslama veri seti kullanılarak test edildi ve lojistik regresyon algoritmasına göre daha yüksek performans gösterdi. Sonuç olarak, bu çalışmada heyelan duyarlılık haritalamasında sığ sinir ağları yöntemi başarıyla uygulanmış ve yöntem gelecekteki çalışmalar için önerilmiştir.

Anahtar Kelimeler

CBS, Heyelan duyarlılık haritalaması, Sığ sinir ağları

Landslide Susceptibility Mapping Using Shallow Neural Networks Model at Refahiye District in Turkey

Öz

Landslides represent a continuous hazard for population and infrastructure. Mapping the landslide susceptibility is an essential issue to avoid the landslides risks. The aim of this paper is to produce a high-accuracy model for landslide susceptibility mapping in Refahiye district in Turkey. The model employed shallow neural networks for landslide susceptibility mapping, while bivariate spearman correlation test was utilized to select the related factors to extract the appropriate data and reduce the computation time of training and mapping. 12 out of 21 spatial factors were selected as relevant factors using Spearman correlation test. Relevant factors are geology, distance from roads, distance from geological faults, distance from water streams, flow direction, aspect, hillshade, heat load index, slope/aspect transformation, site exposure index, compound topographic index, and elevation. The generated dataset was divided into training, validation, and testing datasets using 10-folds cross-validation method. The TrainIm was found to be the best training function with an overall accuracy of 86.3%. The developed NN model was tested using IRIS benchmark dataset and showed higher performance against the logistic regression algorithm. As a result, shallow neural networks method was successfully applied in landslide susceptibility mapping in this study and the method is recommended for future studies.

Anahtar Kelimeler

GIS, Landslide susceptibility mapping, Shallow neural networks

Kaynakça

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