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Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2026 / Cilt: 14 Sayı: 3

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

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

Şevki Öztürk(ÇANKAYA ÜNİVERSİTESİ)0000-0002-0208-2551
A. Ersin Dinçer(ABDULLAH GÜL ÜNİVERSİTESİ)0000-0002-4662-894X
Pages
1327–1341
Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Model performance assessed by ROC analysis yielded an AUC value of 0.946, indicating high predictive capability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:Model performance assessed by ROC analysis yielded an AUC value of 0.946, indicating high predictive capability.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Moloz akması duyarlılığı · Makine öğrenmesi · Heyelan envanteri dönüşümü

Abstract

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.

Özet

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.

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Cite This Article

Öztürk, Ş., Dinçer, A. E. (2026). Precise Prediction of Debris Flow Initiation Zones Derived from General Landslide Inventory Using Machine Learning. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 14 Sayı: 3, 1327–1341. https://doi.org/10.29109/gujsc.1929125

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References (33)

  1. Ma, Z., and Mei, G., “Deep learning for geological hazards analysis: Data, models, applications, and opportunities”, Earth-Science Reviews, 223, (2021). https://doi.org/10.1016/j.earscirev.2021.103858
  2. Yılmaz, K., Dinçer, A. E., Kalpakcı, V., and Öztürk, Ş., “Debris flow modelling and hazard assessment for a glacier area: a case study in Barsem, Tajikistan. Natural Hazards, 115, 2577-2601, (2023). https://doi.org/10.1007/s11069-022-05654-8
  3. Chen, Z., Quan, H., Jin, R., Lin, Z., and Jin, G., “Debris flow susceptibility assessment based on boosting ensemble learning techniques: a case study in the Tumen River basin, China", Stochastic Environmental Research and Risk Assessment, 38(6), 2359-2382, (2024). https://doi.org/10.1007/s00477-024-02683-6
  4. Pal, S. C., Chakrabortty, R., Saha, A., Bozchaloei, S. K., Pham, Q. B., Linh, N. T. T., Anh, D.T., Janizadeh, S., and Ahmadi, K., “Evaluation of debris flow and landslide hazards using ensemble framework of Bayesian- and tree-based models”, Bulletin of Engineering Geology and the Environment, 81(1), (2022). https://doi.org/10.1007/s10064-021-02546-2
  5. Sujatha, E. R., and Sridhar, V. “Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills, Tamil Nadu (India)”, Journal of Earth System Science, 126(8), (2017). https://doi.org/10.1007/s12040-017-0899-7
  6. Zhang, X., Zhang, M., Liu, X., Terfa, B. K., Nam, W. H., Gu, X., Zhang, X., Wang, C., Yang, J., Wang, P., Hu, C., Wu, W., and Chen, N., “Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence”, Natural Hazards, 120, 11485-11525, (2024). https://doi.org/10.1007/s11069-024-06673-3
  7. Yang, L., Ge, Y., Chen, B., Wu, Y., Fu, R., “Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis”, Water, 16(7), (2024). https://doi.org/10.3390/w16070923
  8. Liang, Z., Wang, C., Han, S., Ullah Jan Khan, K., and Liu, Y., “Classification and susceptibility assessment of debris flow based on a semi-quantitative method combination of the fuzzy C-means algorithm, factor analysis and efficacy coefficient”, Natural Hazards and Earth System Sciences, 20(5), 1287-1304, (2020). https://doi.org/10.5194/nhess-20-1287-2020
  9. Qiu, C., Su, L., Zou, Q., and Geng, X., “A hybrid machine-learning model to map glacier-related debris flow susceptibility along Gyirong Zangbo watershed under the changing climate”, Science of the Total Environment, 818, (2022). https://doi.org/10.1016/j.scitotenv.2021.151752
  10. Negi, H. S., Kumar, A., Rao, N. N., Thakur, N. K., Shekhar, M. S., and Snehmani, “Susceptibility assessment of rainfall induced debris flow zones in Ladakh–Nubra region, Indian Himalaya”, Journal of Earth System Science, 129, 30, (2020). https://doi.org/10.1007/s12040-019-1277-4
  11. Li, Z., Chen, J., Tan, C., Zhou, X., Li, Y., and Han, M., “Debris flow susceptibility assessment based on topo-hydrological factors at different unit scales: a case study of Mentougou district, Beijing”, Environmental Earth Sciences, 80, 365, (2021). https://doi.org/10.1007/s12665-021-09665-9
  12. Dikshit, A., Pradhan, B., and Alamri, A. M., “Pathways and challenges of the application of artificial intelligence to geohazards modelling”, Gondwana Research, 100, 290-301, (2021). https://doi.org/10.1016/j.gr.2020.08.007
  13. Gigović, L., Drobnjak, S., and Pamučar, D., “The application of the hybrid GIS spatial multi-criteria decision analysis best–worst methodology for landslide susceptibility mapping”, ISPRS International Journal of Geo-Information, 8(2), 79, (2019). https://doi.org/10.3390/ijgi8020079
  14. Yu, R., Guo, R., Jiang, L., Shao, Y., and Zhou, Z., “Susceptibility assessment of glacier-related debris flow on the southeastern Tibetan Plateau using different hybrid machine learning models”, Science of the Total Environment, 954, (2024). https://doi.org/10.1016/j.scitotenv.2024.176400
  15. Demir, A., Dinçer, A. E., and Dincer, N. N., “Measuring disaster resilience in MENA countries and its impact on disaster losses”, Scientific Reports, 15(1), (2025). https://doi.org/10.1038/s41598-025-27844-7
  16. Xu, X., Qiang, Y., Li, L., Liang, S., Chen, T., Yang, W., Tan, X., Wang, X., and Yang, H., “A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains”, Scientific Reports, 15(1), (2025). https://doi.org/10.1038/s41598-025-11284-4
  17. Ha, H., Luu, C., Bui, Q. D., Nguyen, V. P., Vu, N. T., and Kervyn, M., “Landslide risk assessment using an integrated framework of machine learning algorithms and multi-criteria decision analysis”, Natural Hazards, 121(17), 19723-19759, (2025). https://doi.org/10.1007/s11069-025-07583-8
  18. Tan, L., Guo, J., Mohanarajah, S., and Zhou, K., “Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices”, Natural Hazards, 107(3), 2389-2417, (2021). https://doi.org/10.1007/s11069-020-04429-3
  19. Tehrani, F. S., Calvello, M., Liu, Z., Zhang, L., and Lacasse, S., “Machine learning and landslide studies: recent advances and applications”, Natural Hazards, 114(2), 1197-1245, (2022). https://doi.org/10.1007/s11069-022-05423-7
  20. Tonkal, Ö., and Polat, H., “Traffic classification and comparative analysis with machine learning algorithms in software defined networks”, Gazi University Journal of Science Part C: Design and Technology, 9(1), 71-83, (2021). https://doi.org/10.29109/gujsc.869418
  21. Gülcü, A., and Kuş, Z., “Konvolüsyonel sinir ağlarında hiper-parametre optimizasyonu yöntemlerinin incelenmesi”, Gazi University Journal of Science Part C: Design and Technology, 7(2), 503-522, (2019). https://doi.org/10.29109/gujsc.514483
  22. Chen, L., van Westen, C. J., Hussin, H., Ciurean, R. L., Turkington, T., Chavarro-Rincon, D., and Shrestha, D. P., “Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps”, Geomorphology, 273 (15), 150-167, (2016). https://doi.org/10.1016/j.geomorph.2016.07.041
  23. Sur, U., Singh, P., and Meena, S. R., “Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data”, Geomatics, Natural Hazards and Risk, 11(1), 2176-2209, (2020). https://doi.org/10.1080/19475705.2020.1836038
  24. Li, D., Zhang, H., Li, Y., Zhen, Z., Bu, S., Tang, X., Chen, S., Luo, S., Tian, S., and Xiong, M., “Hazard assessment of debris flow in Guangxi, China based on hydrodynamics mechanism”, Environmental Earth Sciences, 78, 50, (2019). https://doi.org/10.1007/s12665-018-8040-z
  25. Sun, J., Qin, S., Qiao, S., Chen, Y., Su, G., Cheng, Q., Yanqing, Z., and Guo, X., “Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility”. Natural Hazards, 106(1), 881-912, (2021). https://doi.org/10.1007/s11069-020-04498-4
  26. Albahri, A. S., Khaleel, Y. L., Habeeb, M. A., Ismael, R. D., Hameed, Q. A., Deveci, M., Hamood, R.Z., Albahri, O.S., Alamoodi, A.H., and Alzubaidi, L., “A systematic review of trustworthy artificial intelligence applications in natural disasters”, Computers and Electrical Engineering, 118, Part B, (2024). https://doi.org/10.1016/j.compeleceng.2024.109409
  27. Mussina, A., Abdullayeva, A., Blagovechshenskiy, V., Ranova, S., Zeng, Z., Kamalbekova, A., and Aldabergen, U., “Geospatial Mudflow Risk Modeling: Integration of MCDA and RAMMS”, Water, 17(15), 2316, (2025). https://doi.org/10.3390/w17152316
  28. MTA. (2024). MTA Genel Müdürlüğü. https://www.mta.gov.tr/v3.0/hizmetler/heyelan-haritalari (Erişim adresi: 27.02.2026), (2024).
  29. Kumar, A., and Sarkar, R., “Debris Flow Susceptibility Evaluation—A Review”, Iranian Journal of Science and Technology - Transactions of Civil Engineering, 47, 1277-1292, (2023). https://doi.org/10.1007/s40996-022-01000-x
  30. Su, Y., Rong, G., Ma, Y., Chi, J., Liu, X., Zhang, J., and Li, T., “Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province”, Frontiers in Earth Science, (2022). https://doi.org/10.3389/feart.2021.683903
  31. Salehpour Jam, A., Mosaffaie, J., Sarfaraz, F., Shadfar, S., and Akhtari, R., “GIS-based landslide susceptibility mapping using hybrid MCDM models”, Natural Hazards, 108(1), 1025-1046, (2021). https://doi.org/10.1007/s11069-021-04718-5
  32. Rashidi Shikhteymour, S., Borji, M., Bagheri-Gavkosh, M., Azimi, E., Collins, and T. W., “A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods”, Applied Geography, 158, (2023). https://doi.org/10.1016/j.apgeog.2023.103035
  33. Dou, Q., Qin, S., Zhang, Y., Ma, Z., Chen, J., Qiao, S., Hu, X., and Liu, F., “A method for improving controlling factors based on information fusion for debris flow susceptibility mapping: A case study in Jilin province, China”, Entropy, 21(7), (2019). https://doi.org/10.3390/e2107069