Precise Prediction of Debris Flow Initiation Zones Derived from General Landslide Inventory Using Machine Learning

Öz

Debris flows are among the most destructive geotechnical hazards in mountainous regions due to their rapid movement and high destructive potential, often causing significant loss of life and property. The main objective of this study is to derive potential debris flow initiation areas from existing landslide inventory and to predict these areas more accurately using machine learning methods. In this study, large-area landslide polygons obtained from the MTA General Landslide Inventory were transformed into potential debris flow initiation points through geometric transformation and geomorphological threshold filtering. This procedure aims to reduce spatial uncertainties associated with the direct use of conventional landslide inventory data and to generate a more representative dataset for susceptibility modeling. The derived initiation points were analyzed using a machine learning model together with morphometric, environmental, and geological variables. Multicollinearity among the predictor variables was evaluated using Variance Inflation Factor (VIF) analysis. Model performance assessed by ROC analysis yielded an AUC value of 0.946, indicating high predictive capability. 

Anahtar Kelimeler

• Debris flow susceptibility
• Machine learning
• Landslide inventory transformation
• Debris flow modelling

Etik Beyan

No ethical approval was required.

Heyelan Envanterinden Türetilen Moloz Akması Başlangıç Alanlarının Makine Öğrenmesi ile Hassas Tahmini

Öz

Heyelanlar, hızlı hareketleri ve yüksek yıkıcı potansiyelleri nedeniyle dağlık bölgelerdeki en yıkıcı jeoteknik tehlikeler arasında yer almakta ve sıklıkla önemli can ve mal kayıplarına neden olmaktadır. Bu çalışmanın temel amacı, mevcut heyelan envanterinden potansiyel heyelan başlangıç alanlarını belirlemek ve bu alanları makine öğrenmesi yöntemleri kullanarak daha doğru bir şekilde tahmin etmektir. Bu çalışmada, MTA Genel Heyelan Envanterinden elde edilen geniş alanlı heyelan poligonları, geometrik dönüşüm ve jeomorfolojik eşik filtrelemesi yoluyla potansiyel heyelan başlangıç noktalarına dönüştürülmüştür. Bu prosedür, geleneksel heyelan envanteri verilerinin doğrudan kullanımından kaynaklanan mekansal belirsizlikleri azaltmayı ve duyarlılık modellemesi için daha temsili bir veri seti oluşturmayı amaçlamaktadır. Elde edilen başlangıç noktaları, morfometrik, çevresel ve jeolojik değişkenlerle birlikte bir makine öğrenmesi modeli kullanılarak analiz edilmiştir. Tahmin değişkenleri arasındaki çoklu doğrusallık, Varyans Şişirme Faktörü (VIF) analizi kullanılarak değerlendirilmiştir. ROC analizi ile değerlendirilen model performansı, yüksek tahmin kabiliyetini gösteren 0,946’lık bir AUC değeri vermiştir. 

Anahtar Kelimeler

• Moloz akması duyarlılığı
• Makine öğrenmesi
• Heyelan envanteri dönüşümü
• Moloz akması modellemesi

Etik Beyan

Etik beyanı gerektiren bir durum yoktur.

Kaynakça

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