A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP

Şevket Bediroğlu

doi:10.31466/kfbd.1446997

EN TR

A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP

Öz

Geographic Information Systems and machine learning algorithms suggest good alternatives for producing landslide susceptibility maps. In the process of producing these maps with machine learning, alternative data model options exist. Success rate of analyses may change according to the preferred data method. In this study, 6 different machine learning models were created by passing different data models with the XGBoost algorithm. Study area is located in the cities of Ordu and Giresun, Turkiye. 14 different factors and related geographic data layers were used. As a result of the study, the most successful model performance was achieved by taking the average values of all pixels of the combined landslide record polygons (Accuracy=0,88, Precision=0,86, F1 score=0,87). SHAP method was applied for better interpretation of machine learning results The susceptibility map produced with the ideal model, overlapped with 57.556 buildings in the region. The buildings were classified in 4 groups (low, moderate, high, and very high) and mapped, indicating their risk level.

Anahtar Kelimeler

Landslide susceptibility mapping, machine learning, SHAP, GIS, geospatial data model

XGBoost ve SHAP ile Heyelan Duyarlılık Haritalaması İçin Alternatif CBS Veri Modeli Yöntemlerinin Karşılaştırılması

Öz

Coğrafi Bilgi Sistemleri ve makine öğrenimi algoritmaları, heyelan duyarlılık haritalarının üretilmesi için iyi alternatifler önermektedir. Bu haritaların makine öğrenmesi ile üretilmesi sürecinde alternatif veri modeli seçenekleri mevcuttur. Tercih edilen veri yöntemine göre analizlerin başarı oranı değişebilir. Bu çalışmada XGBoost algoritması ile farklı veri modellerini geçerek 6 farklı makine öğrenmesi modeli oluşturulmuştur. Çalışma alanı Türkiye'nin Ordu ve Giresun illerinde bulunmaktadır. 14 farklı faktör ve ilgili coğrafi veri katmanları kullanıldı. Çalışma sonucunda en başarılı model performansı, birleştirilmiş heyelan kayıt poligonlarının tüm piksellerinin ortalama değerleri alınarak elde edilmiştir. Makine öğrenmesi sonuçlarının daha iyi yorumlanması için SHAP yöntemi uygulandı. İdeal model ile üretilen duyarlılık haritası, bölgedeki 57.556 bina ile örtüştü. Binalar 4 grupta (düşük, orta, yüksek ve çok yüksek) sınıflandırılarak risk düzeyleri belirtilerek haritalanmıştır.

Anahtar Kelimeler

Heyelan duyarlılık haritası, Makine öğrenmesi, SHAP, CBS, Coğrafi veri modeli

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Şevket Bediroğlu ^*
0000-0002-7216-6910
Türkiye

Yayımlanma Tarihi

15 Eylül 2024

Gönderilme Tarihi

5 Mart 2024

Kabul Tarihi

28 Mayıs 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 14 Sayı: 3

DOI

https://doi.org/10.31466/kfbd.1446997

IZ

https://izlik.org/JA27XJ96GD

APA

Bediroğlu, Ş. (2024). A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP. Karadeniz Fen Bilimleri Dergisi, 14(3), 1204-1224. https://doi.org/10.31466/kfbd.1446997

AMA

1.Bediroğlu Ş. A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP. KFBD. 2024;14(3):1204-1224. doi:10.31466/kfbd.1446997

Chicago

Bediroğlu, Şevket. 2024. “A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP”. Karadeniz Fen Bilimleri Dergisi 14 (3): 1204-24. https://doi.org/10.31466/kfbd.1446997.

EndNote

Bediroğlu Ş (01 Eylül 2024) A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP. Karadeniz Fen Bilimleri Dergisi 14 3 1204–1224.

IEEE

[1]Ş. Bediroğlu, “A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP”, KFBD, c. 14, sy 3, ss. 1204–1224, Eyl. 2024, doi: 10.31466/kfbd.1446997.

ISNAD

Bediroğlu, Şevket. “A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP”. Karadeniz Fen Bilimleri Dergisi 14/3 (01 Eylül 2024): 1204-1224. https://doi.org/10.31466/kfbd.1446997.

JAMA

1.Bediroğlu Ş. A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP. KFBD. 2024;14:1204–1224.

MLA

Bediroğlu, Şevket. “A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP”. Karadeniz Fen Bilimleri Dergisi, c. 14, sy 3, Eylül 2024, ss. 1204-2, doi:10.31466/kfbd.1446997.

Vancouver

1.Şevket Bediroğlu. A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP. KFBD. 01 Eylül 2024;14(3):1204-2. doi:10.31466/kfbd.1446997

Cited By

The influence of sampling strategies on shallow landslide susceptibility modelling in a mountainous area

Landslides

https://doi.org/10.1007/s10346-025-02646-0

Karadeniz Fen Bilimleri Dergisinde yayınlanan makaleler Creative Commons Attribution-NonCommercial 4.0 International kapsamında lisanslanmıştır.

A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP

Öz

Anahtar Kelimeler

XGBoost ve SHAP ile Heyelan Duyarlılık Haritalaması İçin Alternatif CBS Veri Modeli Yöntemlerinin Karşılaştırılması

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

The influence of sampling strategies on shallow landslide susceptibility modelling in a mountainous area