Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye

Muhammed Mustafa Özdel; Melike Durak; Serkan Sabancı; İsa Cürebal

doi:10.46453/jader.2035778

TR EN

Güneydoğu Türkiye'de Heyelan Duyarlılığına Yönelik Karşılaştırmalı Makine Öğrenmesi Yaklaşımları

Abstract

Heyelanlar, dağlık bölgelerdeki en yıkıcı doğal afetler arasında yer almakta olup ciddi ekonomik kayıplara ve altyapı hasarlarına yol açmaktadır. Güvenilir duyarlılık haritalarının oluşturulması, özellikle Güneydoğu Türkiye gibi tektonik açıdan aktif bölgelerde, tehlikelerin azaltılması ve sürdürülebilir arazi kullanım planlaması açısından büyük önem taşımaktadır. Bu çalışmanın amacı; yaygın olarak kullanılan altı makine öğrenmesi algoritmasının —Destek Vektör Makinesi (SVM), Lojistik Regresyon (LR), Rastgele Orman (RF), Yapay Sinir Ağı (ANN), Naive Bayes (NB) ve Maksimum Entropi (MaxEnt)— ve bunlarla birlikte bir Topluluk (Ensemble) modelinin tahmin performanslarını ve mekânsal tutarlılıklarını, aynı jeomorfolojik ve çevresel koşullar altında karşılaştırmaktır. Topografik, jeolojik, hidrolojik ve arazi örtüsü veri setlerinden elde edilen on bir duyarlılık faktörü kullanılarak CBS tabanlı bir yaklaşım uygulanmıştır. Model eğitimi ve doğrulama için saha gözlemleriyle doğrulanmış bir heyelan envanteri kullanılmıştır. Model performansı, ROC (Receiver Operating Characteristic) analizi ve AUC (Area Under the Curve) kullanılarak değerlendirilmiştir. Tüm modeller yüksek tahmin doğruluğu sergilemiştir (AUC = 0,877–0,959). En yüksek performansı NB modeli sergilerken (AUC = 0,959), Ensemble modeli mekânsal açıdan en tutarlı duyarlılık paternlerini üretmiştir. Yüksek ve çok yüksek duyarlılık sınıfları, istikrarlı bir şekilde dik yamaçlar ve zayıf litolojik birimlerle ilişkili bulunmuştur. Karşılaştırmalı makine öğrenmesi yaklaşımları, bölgesel heyelan duyarlılığının değerlendirilmesinde güvenilir ve farklı alanlara uygulanabilir araçlar sunmaktadır. Elde edilen sonuçlar, duyarlılık haritalamasında algoritma seçiminin ve topluluk (ensemble) stratejilerinin önemini vurgulamaktadır.

Keywords

Heyelan Duyarlılık Haritalaması, Makine Öğrenmesi, Alkumru, Tehlike Azaltma

Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye

Abstract

Landslides are among the most destructive natural hazards in mountainous regions, causing severe economic losses and infrastructure damage. Reliable susceptibility mapping is essential for hazard mitigation and sustainable land-use planning, particularly in tectonically active areas such as southeastern Türkiye. This study aims to compare the predictive performance and spatial consistency of six widely used machine learning algorithms—Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Naïve Bayes (NB), and Maximum Entropy (MaxEnt)—together with an Ensemble model, under identical geomorphological and environmental conditions. A GIS-based framework was applied using eleven conditioning factors derived from topographic, geological, hydrological, and land-cover datasets. A landslide inventory verified by field observations was used for model training and validation. Model performance was assessed using Receiver Operating Characteristic (ROC) analysis and the Area Under the Curve (AUC). All models achieved high predictive accuracy (AUC = 0.877–0.959). The NB model performed best (AUC = 0.959), while the Ensemble model produced the most spatially consistent susceptibility patterns. High- and very high-susceptibility zones were consistently associated with steep slopes and weak lithological units. Comparative machine learning approaches provide reliable and transferable tools for regional landslide susceptibility assessment. The results highlight the importance of algorithm selection and ensemble strategies in susceptibility mapping.

Keywords

Landslide Susceptibility Mapping, Machine Learning, Alkumru, Hazard Mitigation

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Details

Primary Language

English

Subjects

Geographic Information Systems

Journal Section

Research Article

Authors

Muhammed Mustafa Özdel
0000-0003-0715-4566
Türkiye

Melike Durak ^*
0000-0003-3102-9132
Türkiye

Serkan Sabancı
0009-0002-6008-5213
Türkiye

İsa Cürebal
0000-0002-3449-1595
Türkiye

Publication Date

October 10, 2026

Submission Date

September 8, 2026

Acceptance Date

September 30, 2026

Published in Issue

Year 2026 Number: 17

DOI

https://doi.org/10.46453/jader.2035778

IZ

https://izlik.org/JA29ZD47UP

APA

Özdel, M. M., Durak, M., Sabancı, S., & Cürebal, İ. (2026). Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye. Jeomorfolojik Araştırmalar Dergisi, 17. https://doi.org/10.46453/jader.2035778

AMA

1.Özdel MM, Durak M, Sabancı S, Cürebal İ. Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye. JADER. 2026;(17). doi:10.46453/jader.2035778

Chicago

Özdel, Muhammed Mustafa, Melike Durak, Serkan Sabancı, and İsa Cürebal. 2026. “Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye”. Jeomorfolojik Araştırmalar Dergisi, nos. 17. https://doi.org/10.46453/jader.2035778.

EndNote

Özdel MM, Durak M, Sabancı S, Cürebal İ (October 1, 2026) Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye. Jeomorfolojik Araştırmalar Dergisi 17

IEEE

[1]M. M. Özdel, M. Durak, S. Sabancı, and İ. Cürebal, “Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye”, JADER, no. 17, Oct. 2026, doi: 10.46453/jader.2035778.

ISNAD

Özdel, Muhammed Mustafa - Durak, Melike - Sabancı, Serkan - Cürebal, İsa. “Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye”. Jeomorfolojik Araştırmalar Dergisi. 17 (October 1, 2026). https://doi.org/10.46453/jader.2035778.

JAMA

1.Özdel MM, Durak M, Sabancı S, Cürebal İ. Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye. JADER. 2026. doi:10.46453/jader.2035778.

MLA

Özdel, Muhammed Mustafa, et al. “Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye”. Jeomorfolojik Araştırmalar Dergisi, no. 17, Oct. 2026, doi:10.46453/jader.2035778.

Vancouver

1.Muhammed Mustafa Özdel, Melike Durak, Serkan Sabancı, İsa Cürebal. Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye. JADER. 2026 Oct. 1;(17). doi:10.46453/jader.2035778

Aim & Scope

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**Sample MS Word File (Download)**

To avoid problems in acceptance and peer-review assignment, careful attention to these requirements is strongly advised.

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Research Articles
- Turkish Title / English Title
- 250–300 word Turkish Abstract + 4–5 Keywords
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Structure:
- Turkish Title / English Title
- Abstract (Turkish) + 4–5 Keywords
- Abstract (English) + 4–5 Keywords
- Extended Abstract (minimum 1000 words; not required for manuscripts written entirely in English)
- Introduction
- Findings
- Conclusion – Discussion – Recommendations
- References

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Title
Titles must be provided in both Turkish and English, kept as concise as possible, and accurately reflect the content of the manuscript.

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Main Text
Submissions must be in *.doc* or *.docx* format, written in *PT Sans* font, 12-point size, single-spaced, and single-column.
Mathematical expressions and equations must be numbered, aligned to the left, with equation numbers in parentheses aligned to the right.

Order of sections:
- Title (with English equivalent)
- Turkish Abstract + Keywords
- English Abstract + Keywords
- Main Text (Introduction, Materials and Methods, Results, Conclusion, Discussion, Recommendations)
- Acknowledgments
- References and Appendices (if any)

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Abstract
Abstracts must be prepared in both Turkish and English, not exceeding 300 words. They should include the aim, study area (if applicable), materials and methods, main findings, and main conclusions. At the end of the abstract, 4–5 keywords must be provided.
For manuscripts written entirely in English, a Turkish abstract is not required.
Abstracts must be formatted in 8-point font, justified alignment.

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References and Citations
The **APA citation style** must be used. APA guidelines can be downloaded [here].
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The reference list must be arranged alphabetically by author surname, and within each author’s works, chronologically (from oldest to newest).

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The publication processes applied in the *Journal of Geomorphological Research (JADER)* form the foundation for the impartial and respected development and dissemination of knowledge. These processes directly reflect the quality of the work of authors and their supporting institutions. Peer-reviewed studies embody and reinforce the scientific method. Therefore, it is essential that all stakeholders in the process (authors, readers, researchers, publishers, reviewers, and editors) adhere to ethical standards. Within the framework of publication ethics, JADER expects all stakeholders to uphold the following responsibilities.

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### 1. Authors’ Ethical Responsibilities
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### 2. Editors’ Ethical Duties and Responsibilities
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### 3. Reviewers’ Responsibilities
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### 4. Publisher’s Ethical Responsibilities
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### 5. Plagiarism Detection Policy
All manuscripts are screened using **intihal.net** and **iThenticate** to confirm originality and absence of plagiarism.

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### 6. Reporting Ethical Misconduct
If you notice a significant error or unethical behavior in a published article, please contact:
📧 **jader@jd.org.tr** or **curebal@balikesir.edu.tr**.
Complaints are welcomed as opportunities for improvement, and JADER aims to respond promptly and constructively.

Indexes

Journal Boards

Scientific Publication Coordinator

Musa ULUDAĞ

Türkiye Physical Geography, Physical Geography

Editor in Chief

İsa CÜREBAL

Physical Geography and Environmental Geology, Natural Hazards, Drainage, Geomorphology and Earth Surface Processes, Quaternary Environments

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Mesut ŞIMŞEK

Geomorphology and Earth Surface Processes

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Geographic Information Systems, Geomorphology and Earth Surface Processes, Physical Geography

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Cengiz YILDIRIM

Geomorphology and Earth Surface Processes, Quaternary Environments, Structural Geology and Tectonics

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Geographic Information Systems, Geomorphology and Earth Surface Processes

Muhammed Mustafa ÖZDEL

Conservation and Biodiversity, Ecological Impacts of Climate Change and Ecological Adaptation, Climatology

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Ahmet Evren ERGINAL

Ecological Applications, Marine Geology and Geophysics , General Geology, Geoarcheology , Geochronology

Hüseyin TUROGLU

Regional Planning, Watershed Management, Hydrography, Geomorphology and Earth Surface Processes, Climatology

Uğur DOĞAN

Quaternary Environments, Palaeogeography, Physical Geography

Murat TÜRKEŞ

Biogeography and Phylogeography, Environmental Education and Extension, Physical Geography and Environmental Geology, Biogeography, Watershed Management, Hydrography, Geomorphology and Earth Surface Processes, Physical Geography, Climatology

Mustafa Hakan YIĞITBAŞIOĞLU

Geomorphology and Earth Surface Processes, Physical Geography

M. Kirami ÖLGEN

Health Sciences, Physical Geography

Tuncer DEMIR

Physical Geography and Environmental Geology, Watershed Management, Hydrography, Geomorphology and Earth Surface Processes, Physical Geography, Climatology

Abdullah SOYKAN

Geomorphology and Earth Surface Processes, Physical Geography

Jeomorfolojik Araştırmalar Dergisi ( JADER ) - Journal of Geomorphological Researches

TR Dizin - DOAJ - Crossref - Google Scholar

Turkish Society for Geomorphology

www.jd.org.tr

Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye

Güneydoğu Türkiye'de Heyelan Duyarlılığına Yönelik Karşılaştırmalı Makine Öğrenmesi Yaklaşımları

Abstract

Keywords

Comparative Machine Learning Approaches to Landslide Susceptibility in Southeastern Türkiye

Abstract

Keywords

References

Details

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Journal Section

Authors

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Aim & Scope

Author Guidelines

Ethical Principles and Publication Policy

Indexes

Citation Indexes

Other Indexes

Journal Boards

Scientific Publication Coordinator

Editor in Chief

Editors

Language Editor

Layout Editor

Editorial Adviser Board