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

Yıl 2024, Cilt: 14 Sayı: 3, 1204 – 1224, 15.09.2024

Şevket Bediroğlu

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

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

  • Abedini M, Ghasemian B, Shirzadi A, Shahabi H, Chapi K, Pham BT, Bin Ahmad B, and Tien Bui D. 2019. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment. Geocarto International. 34(13):1427-1457.
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  • Aghlmand, M., Onur M. İ. and Talaei R. (2020). Heyelan Duyarlılık Haritalarının Üretilmesinde Analitik Hiyerarşi Yönteminin ve Coğrafi Bilgi Sistemlerinin Kullanımı. Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, S. 224-230, Nisan 2020
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  • Arabameri, A., Chandra Pal, S., Rezaie, F., Chakrabortty, R., Saha, A., Blaschke, T., di Napoli, M., Ghorbanzadeh, O., and Thi Ngo, P. T. (2022). Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International, 37(16), 4594–4627. https://doi.org/10.1080/10106049.2021.1892210
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  • Breiman L. (2001). Random Forests. Machine Learning. Kluwer Academic Publishers. 45(1):5-32.
  • Chang Z., Du Z., Zhang F., Huang .F, Chen J., Li W., and Guo Z. (2020). Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sensing. 12(3).
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  • Chen W., Peng J., Hong H., Shahabi H., Pradhan B., Liu J., Zhu AX., Pei X., and Duan Z. (2018). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of The Total Environment. 626:1121-1135.
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  • Dai FC., Lee CF., and Zhang XH. (2001). GIS-based geo-environmental evaluation for urban land-use planning: a case study. Engineering Geology. 61(4):257-271.
  • Dehnavi A., Aghdam IN., Pradhan B., and Morshed Varzandeh MH. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA. 135:122-148.
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A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP

Yıl 2024, Cilt: 14 Sayı: 3, 1204 – 1224, 15.09.2024

Şevket Bediroğlu

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

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

Kaynakça

  • Abedini M, Ghasemian B, Shirzadi A, Shahabi H, Chapi K, Pham BT, Bin Ahmad B, and Tien Bui D. 2019. A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment. Geocarto International. 34(13):1427-1457.
  • Aghdam IN., Varzandeh MHM., and Pradhan B. (2016). Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environmental Earth Sciences. 75(7):553.
  • Aghlmand, M., Onur M. İ. and Talaei R. (2020). Heyelan Duyarlılık Haritalarının Üretilmesinde Analitik Hiyerarşi Yönteminin ve Coğrafi Bilgi Sistemlerinin Kullanımı. Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, S. 224-230, Nisan 2020
  • Akinci H., Kilicoglu C., and Dogan S. (2020). Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey. ISPRS International Journal of Geo-Information. 2020; 9(9):553
  • Althuwaynee OF., Pradhan B., Park H-J., and Lee JH. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA. 114:21-36.
  • Althuwaynee OF., Pradhan B., and Lee S. (2016). A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing. 37(5):1190-1209.
  • Arabameri, A., Chandra Pal, S., Rezaie, F., Chakrabortty, R., Saha, A., Blaschke, T., di Napoli, M., Ghorbanzadeh, O., and Thi Ngo, P. T. (2022). Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International, 37(16), 4594–4627. https://doi.org/10.1080/10106049.2021.1892210
  • Atkinson PM., and Massari R. (1998). Generalised Linear Modelling of Susceptibility to Landsliding in the Central Apennines, ITALY. Computers & Geosciences. 24(4):373-385.
  • Beguería S. (2006). Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management. Natural Hazards. 37(3):315-329.
  • Breiman L. (2001). Random Forests. Machine Learning. Kluwer Academic Publishers. 45(1):5-32.
  • Chang Z., Du Z., Zhang F., Huang .F, Chen J., Li W., and Guo Z. (2020). Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sensing. 12(3).
  • Ciampalini, A., Bardi, F., Bianchini, S., Frodella, W., del Ventisette, C., Moretti, S., and Casagli, N. (2014). Analysis of building deformation in landslide area using multisensor PSInSARTM technique. International Journal of Applied Earth Observation and Geoinformation, 33, 166–180.
  • Chen W., Peng J., Hong H., Shahabi H., Pradhan B., Liu J., Zhu AX., Pei X., and Duan Z. (2018). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of The Total Environment. 626:1121-1135.
  • Chen W. and Li Y. (2020). GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. CATENA. 195:104777.
  • Chen T. and Guestrin C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). Association for Computing Machinery, New York, NY, USA, 785–794.
  • Ching J. and Phoon K-K. (2019). Constructing Site-Specific Multivariate Probability Distribution Model Using Bayesian Machine Learning. Journal of Engineering Mechanics. 145(1):04018126.
  • Constantin M., Bednarik M., Jurchescu MC., and Vlaicu M. (2011). Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental Earth Sciences. 63(2):397-406.
  • Dai FC., Lee CF., and Zhang XH. (2001). GIS-based geo-environmental evaluation for urban land-use planning: a case study. Engineering Geology. 61(4):257-271.
  • Dehnavi A., Aghdam IN., Pradhan B., and Morshed Varzandeh MH. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA. 135:122-148.
  • De Sy V., Schoorl JM., Keesstra SD., Jones KE., and Claessens L. (2013). Landslide model performance in a high resolution small-scale landscape. Geomorphology. 190:73-81.
  • Fang, Z., Wang, Y., Peng, L., and Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 139, 104470.
  • Fanos, A. M. and Pradhan, B. (2019). A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS. CATENA, 172, 435–450.
  • Feizizadeh B., Shadman Roodposhti M., Jankowski P., and Blaschke T. (2014). A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Computers & Geosciences. 73:208-221.
  • Froude M. and Petley D. (2018). Global fatal landslide occurrence 2004 to 2016. Natural Hazards and Earth System Sciences Discussions.1-44.
  • Fu, S., Chen, L., Woldai, T., Yin, K., Gui, L., Li, D., Du, J., Zhou, C., Xu, Y., and Lian, Z. (2020). Landslide hazard probability and risk assessment at the community level: a case of western Hubei, China. Nat. Hazards Earth Syst. Sci., 20(2), 581–601.
  • Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE international conference on computer vision 1440–1448.
  • Greedy F. J. function approximation: a gradient boosting machine. Annals of Statistics, 29(5):1189{1232, 2001.
  • Gorsevski, P.V., Gessler, P.E., Foltz, R.B. and Elliot, W.J. (2006), Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis. Transactions in GIS, 10: 395-415.
  • Guzzetti F., Reichenbach P., Ardizzone F., Cardinali M., and Galli M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology. 81(1):166-184.
  • Hong H., Miao Y., Liu J., and Zhu AX. (2019). Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. CATENA. 176:45-64.
  • Hong H., Naghibi SA., Pourghasemi HR., and Pradhan B. (2016). GIS-based landslide spatial modeling in Ganzhou City, China. Arabian Journal of Geosciences. 9(2):112.
  • Hong H., Pradhan B., Sameen MI., Kalantar B., Zhu A., and Chen W. (2018). Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides. 15(4):753-772.
  • Hong H., Liu J., and Zhu AX. (2020). Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Science of The Total Environment. 718:137231.
  • Hong, H. (2023). Assessing landslide susceptibility based on hybrid Best-first decision tree with ensemble learning model. Ecological Indicators, 147, 109968.
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  • Orhan, O., Bilgilioglu, S. S., Kaya, Z., Ozcan, A. K., and Bilgilioglu, H. (2022). Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37(10), 2795–2820. https://doi.org/10.1080/10106049.2020.1837258
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Toplam 81 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yer Bilimleri ve Jeoloji Mühendisliği (Diğer)
BölümMakaleler
Yazarlar

Şevket Bediroğlu GAZİANTEP ÜNİVERSİTESİ, MİMARLIK FAKÜLTESİ 0000-0002-7216-6910 Türkiye

Yayımlanma Tarihi15 Eylül 2024
Gönderilme Tarihi5 Mart 2024
Kabul Tarihi28 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 14 Sayı: 3

Kaynak Göster

APABediroğlu, 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

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