VAN İLİ HEYELAN DUYARLILIĞININ FREKANS ORANI YÖNTEMİYLE ANALİZİ

Abstract

Bu çalışmada, Van ili heyelan duyarlılığı Coğrafi Bilgi Sistemleri ortamında Frekans Oranı yöntemi kullanılarak belirlenmiştir. Heyelan duyarlılık analizinde; litoloji, fay hatlarına uzaklık, arazi kullanımı örtüsü, yükseklik, eğim, bakı ve genel eğrilik faktörleri değerlendirmeye alınmıştır. Heyelan envanterinin %70’i eğitim verisi, %30’u doğrulama verisi olarak kullanılmıştır. Heyelan duyarlılık sonuçlarından kategorik heyelan duyarlılık haritasının oluşturulmasında Eşit Aralıklı, Doğal Aralıklı, Geometrik Aralıklı ve Kuantil Sınıflandırma teknikleri kullanılmış ve heyelan duyarlılığı Çok Yüksek, Yüksek, Orta, Düşük ve Çok Düşük olmak üzere beş sınıfa kategorilendirilmiştir. ROC (İşlem Karakteristik Eğrisi) analizi ve SCAI (Doğrulama Pikseli Alan İndeksi) indeksi ile heyelan duyarlılık haritalarının doğruluk değerlendirmesi gerçekleştirilmiş ve Doğal Aralıklı Sınıflandırma yönteminin daha iyi sonuç verdiği tespit edilmiştir. Doğal Aralıklı Sınıflandırma yöntemi sonucuna göre ilin %17,2’si Çok Yüksek, %27,5’i Yüksek, %27,7’si Orta, %20,0’ı Düşük ve %7,6’sı Çok Düşük heyelan duyarlılığı göstermektedir. Heyelan duyarlılık haritasının arazi kullanımı/örtüsü katmanı ile çakıştırılması sonucunda ilde yerleşim ve endüstriyel alanların 0,2 km2’sinin Çok Yüksek, 3,6 km2’sinin Yüksek heyelan duyarlılığında olduğu belirlenmiştir. Sonuç olarak, Frekans Oranı yöntemiyle elde edilen analiz sonuçlarından farklı sınıflandırma teknikleri ile optimum kategorik heyelan haritasının elde edilebileceği ve gelecekteki muhtemel heyelanlar için tehlike altında bulunan alanların öngörüsünde kullanılarak afet yönetimi ve planlama çalışmalarına entegre edilebileceği görülmüştür.

Keywords

• Heyelan Duyarlılığı
• Frekans Oranı
• Coğrafi Bilgi Sistemleri
• Doğal aralıklı sınıflandırma
• ROC analizi
• SCAI indeksi

Analysis of Landslide Susceptibility of Van Province Using Frequency Ratio Method

Abstract

In this study, the landslide susceptibility of Van province was determined using the Frequency Ratio method in the Geographical Information Systems environment. In the landslide susceptibility analysis; lithology, distance to fault lines, land use/cover, elevation, slope, aspect, and general curvature were taken into consideration. 70% of the landslide inventory was used as training data and 30% as test data. To obtain the categorical landslide susceptibility map from the landslide susceptibility analysis results, classification techniques of Equal Interval, Natural Breaks, Geometric Interval, and Quantile were used and landslide susceptibility was categorized into five classes as Very High, High, Medium, Low, and Very Low. The accuracy of the landslide susceptibility maps was evaluated by ROC (Receiver Operating Characteristic) analysis and SCAI (Seed Cell Area Index) index, and it was determined that the Natural Breaks Classification method gave better results. According to the result of the Natural Breaks Classification method, 17.2% of the province had Very High, 27.5% High, 27.7% Medium, 20.0% Low, and 7.6% Very Low landslide susceptibility. As a result of overlapping the landslide susceptibility map with the land use/cover layer, it was determined that 0.2 km2 of the residential and industrial areas in the province had Very High and 3.6 km2 had High landslide susceptibility. As a result, it has been seen that the optimum categorical landslide map can be selected by different classification techniques from the analysis results obtained by the Frequency Ratio method, and it can be integrated into disaster management and planning studies by using it in the prediction of endangered areas for possible future landslides. 

Keywords

• Landslide Susceptibility
• Frequency Ratio
• Geographical Information Systems
• SCAI
• Natural breaks classification
• ROC

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