Abstract
Across the globe, landslides have been recognized as one of the most detrimental geological calamities, especially in hilly terrains. However, the correct determination of landslide-prone fields remained a challenging task due to the nonlinear, complex, and inconsistent nature of landslides. Therefore, it is essential to apply methods with superior capabilities in dealing with real-world problems for properly delineating potential landslide zones. Recently, ensemble learning techniques have been drawn intense interest in landslide susceptibility mapping studies due to their distinct advantages. This present work intended to propose natural gradient boosting (NGBoost), a novel member of the ensemble learning family, for modeling landslide susceptibility for Macka County of Trabzon province, Turkey. The predictive performance of NGBoost was compared to ensemble-based machine learning methods, namely random forest (RF) and XGBoost using five accuracy metrics including overall accuracy (OA), F1 score, Kappa coefficient, area under curve (AUC) value, and root-mean-square error to evaluate its competence and robustness. Besides, SHAP based on the game theory approach was implemented to interpret the influences of the predisposing factors on the produced model. Results indicated that the NGBoost method utilized for landslide susceptibility mapping problem for the first time had the greatest predictive ability (AUC = 0.898), followed by XGBoost (AUC = 0.871) and RF (AUC = 0.863), and outperformed the XGBoost and RF by approximately 6% in terms of OA. McNemar’s statistical significance test results also confirmed the superiority of the proposed NGBoost method over the RF and XGBoost algorithms.
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References
Schuster, R.L.: Socioeconomic significance of landslides. Spec. Rep. Natl. Res. Counc. Transp. Res. Board. 247, 12–35 (1996)
Dilley, M.; Chen, R.S.; Deichmann, U.; Lerner-Lam, A.; Arnold, M.; Agwe, J.; Buys, P.; Kjekstad, O.; Lyon, B.; Yetman, G.: Natural Disaster Hotspots: A Global Risk Analysis. World Bank Publications, Washington, DC (2005)
Safaei, M.; Omar, H.; Huat, B.K.; Yousof, Z.B.M.; Ghiasi, V.: Deterministic rainfall induced landslide approaches, advantage and limitation. Electron. J. Geotech. Eng. 16, 1619–1650 (2011)
Kjekstad, O.; Highland, L.: Economic and social impacts of landslides. In: Zhou, L.; Ooi, B.; Meng, X. (Eds.) Landslides: Disaster Risk Reduction, pp. 573–587. Springer, Berlin (2009)
Ildir, B.: Turkiyede heyelanlarin dagilimi ve afetler yasasi ile ilgili uygulamalar (Landslide distribution in Turkey and applications based on disasters regulation). In: Onalp, A. (ed.) Proceedings of 2nd National Landslide Symposium Turkey, Sakarya University. pp. 1–9 (1995)
Hasekioğulları, G.D.; Ercanoglu, M.: A new approach to use AHP in landslide susceptibility mapping: A case study at Yenice (Karabuk, NW Turkey). Nat. Hazards. 63, 1157–1179 (2012). https://doi.org/10.1007/s11069-012-0218-1
Görüm, T.; Fidan, S.: Spatiotemporal variations of fatal landslides in Turkey. Landslides 18, 1691–1705 (2021). https://doi.org/10.1007/s10346-020-01580-7
Yalcin, A.: Environmental impacts of landslides: a case study from East Black Sea region, Turkey. Environ. Eng. Sci. 24, 821–833 (2007). https://doi.org/10.1089/ees.2006.0161
Akgün, A.; Bulut, F.: GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environ. Geol. 51, 1377–1387 (2007). https://doi.org/10.1007/s00254-006-0435-6
Yalcin, A.: A geotechnical study on the landslides in the Trabzon Province, NE, Turkey. Appl. Clay Sci. 52, 11–19 (2011). https://doi.org/10.1016/j.clay.2011.01.015
Kavzoglu, T.; Kutlug Sahin, E.; Colkesen, I.: Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng. Geol. 192, 101–112 (2015). https://doi.org/10.1016/j.enggeo.2015.04.004
Colkesen, I.; Sahin, E.K.; Kavzoglu, T.: Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J. African Earth Sci. 118, 53–64 (2016). https://doi.org/10.1016/j.jafrearsci.2016.02.019
Kutlug Sahin, E.; Ipbuker, C.; Kavzoglu, T.: Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping. Geocarto Int. 32, 956–977 (2017). https://doi.org/10.1080/10106049.2016.1170892
Akinci, H.; Kilicoglu, C.; Dogan, S.: Random forest-based landslide susceptibility mapping in coastal regions of artvin, Turkey. ISPRS Int. J. Geo-Inf. 9, 4993 (2020). https://doi.org/10.3390/ijgi9090553
Sezer, E.A.; Nefeslioglu, H.A.; Osna, T.: An expert-based landslide susceptibility mapping (LSM) module developed for Netcad Architect Software. Comput. Geosci. 98, 26–37 (2017). https://doi.org/10.1016/j.cageo.2016.10.001
Akgun, A.; Dag, S.; Bulut, F.: Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ. Geol. 54, 1127–1143 (2008). https://doi.org/10.1007/s00254-007-0882-8
Ercanoglu, M.; Gokceoglu, C.: Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng. Geol. 75, 229–250 (2004). https://doi.org/10.1016/j.enggeo.2004.06.001
Kavzoglu, T.; Sahin, E.K.; Colkesen, I.: Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11, 425–439 (2013). https://doi.org/10.1007/s10346-013-0391-7
Gokceoglu, C.; Sonmez, H.; Nefeslioglu, H.A.; Duman, T.Y.; Can, T.: The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng. Geol. 81, 65–83 (2005). https://doi.org/10.1016/j.enggeo.2005.07.011
Pradhan, B.; Jebur, M.N.: Spatial prediction of landslide-prone areas through k-nearest neighbor algorithm and logistic regression model using high resolution airborne laser scanning data. In: Pradhan, B. (Ed.) Laser Scanning Applications in Landslide Assessment, pp. 151–165. Springer (2017)
Chen, L.C.; Liu, Y.C.; Chan, K.C.: Integrated community-based disaster management program in taiwan: a case study of shang-an village. Nat. Hazards. 37, 209–223 (2006). https://doi.org/10.1007/s11069-005-4669-5
Regmi, N.R.; Giardino, J.R.; Vitek, J.D.: Assessing susceptibility to landslides: using models to understand observed changes in slopes. Geomorphology 122, 25–38 (2010). https://doi.org/10.1016/j.geomorph.2010.05.009
Dai, F.C.; Lee, C.F.; Ngai, Y.Y.: Landslide risk assessment and management: an overview. Eng. Geol. 64, 65–87 (2002). https://doi.org/10.1016/S0013-7952(01)00093-X
Guzzetti, F.; Carrara, A.; Cardinali, M.; Reichenbach, P.: Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31, 181–216 (1999). https://doi.org/10.1016/S0169-555X(99)00078-1
Pradhan, B.: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput. Geosci. 51, 350–365 (2013). https://doi.org/10.1016/j.cageo.2012.08.023
Kavzoglu, T.; Kutlug Sahin, E.; Colkesen, I.: An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat. Hazards. 76, 471–496 (2015). https://doi.org/10.1007/s11069-014-1506-8
Lee, S.; Sambath, T.: Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ. Geol. 50, 847–855 (2006). https://doi.org/10.1007/s00254-006-0256-7
Yilmaz, I.: Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput. Geosci. 35, 1125–1138 (2009). https://doi.org/10.1016/j.cageo.2008.08.007
Yao, X.; Tham, L.G.; Dai, F.C.: Landslide susceptibility mapping based on Support Vector Machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101, 572–582 (2008). https://doi.org/10.1016/j.geomorph.2008.02.011
Kalantar, B.; Pradhan, B.; Amir Naghibi, S.; Motevalli, A.; Mansor, S.: Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geom. Nat. Hazards Risk. 9, 49–69 (2018). https://doi.org/10.1080/19475705.2017.1407368
Tien Bui, D.; Pradhan, B.; Lofman, O.; Revhaug, I.: Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and naïve bayes models. Math. Probl. Eng. (2012). https://doi.org/10.1155/2012/974638
Pourghasemi, H.R.; Rahmati, O.: Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA 162, 177–192 (2018). https://doi.org/10.1016/j.catena.2017.11.022
Gómez, H.; Kavzoglu, T.: Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin. Venezuela. Eng. Geol. 1–2, 1–27 (2005). https://doi.org/10.1016/j.enggeo.2004.10.004
Lee, S.; Ryu, J.H.; Kim, I.S.: Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: Case study of Youngin, Korea. Landslides 4, 327–338 (2007). https://doi.org/10.1007/s10346-007-0088-x
Kavzoglu, T.; Teke, A.; Yilmaz, E.O.: Shared blocks-based ensemble deep learning for shallow landslide susceptibility mapping. Remote Sens. 13, 4776 (2021). https://doi.org/10.3390/rs13234776
Dong, X.; Yu, Z.; Cao, W.; Shi, Y.; Ma, Q.: A survey on ensemble learning. Front. Comput. Sci. 14, 241–258 (2020). https://doi.org/10.1007/s11704-019-8208-z
Gao, X.; Shan, C.; Hu, C.; Niu, Z.; Liu, Z.: An adaptive ensemble machine learning model for intrusion detection. IEEE Access. 7, 82512–82521 (2019). https://doi.org/10.1109/ACCESS.2019.2923640
Sagi, O.; Rokach, L.: Ensemble learning: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8, e1249, 1–18 (2018). https://doi.org/10.1002/widm.1249
Fang, Z.; Wang, Y.; Peng, L.; Hong, H.: A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int. J. Geogr. Inf. Sci. 35, 321–347 (2021). https://doi.org/10.1080/13658816.2020.1808897
Pham, B.T.; Nguyen-Thoi, T.; Qi, C.; Phong, T.V.; Dou, J.; Ho, L.S.; Le, H.V.; Prakash, I.: Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. CATENA 195, 104805 (2020). https://doi.org/10.1016/j.catena.2020.104805
MTA Yerbilimleri Harita Goruntuleyici ve Cizim Editoru, Available online: http://yerbilimleri.mta.gov.tr/. Accessed 3 Mar 2021
Tsangaratos, P.; Ilia, I.; Hong, H.; Chen, W.; Xu, C.: Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14, 1091–1111 (2017). https://doi.org/10.1007/s10346-016-0769-4
Dou, J.; Yunus, A.P.; Tien Bui, D.; Merghadi, A.; Sahana, M.; Zhu, Z.; Chen, C.W.; Khosravi, K.; Yang, Y.; Pham, B.T.: Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci. Total Environ. 662, 332–346 (2019). https://doi.org/10.1016/j.scitotenv.2019.01.221
Can, R.; Kocaman, S.; Gokceoglu, C.: A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk dam, Turkey. Appl. Sci. 11, 4993 (2021). https://doi.org/10.3390/app11114993
Lima, P.; Steger, S.; Glade, T.: Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria. Landslides 18, 3531–3546 (2021). https://doi.org/10.1007/s10346-021-01693-7
Yanar, T.; Kocaman, S.; Gokceoglu, C.: Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS Int. J. Geo-Inf. 9, 114 (2020). https://doi.org/10.3390/ijgi9020114
Kocaman, S.; Gokceoglu, C.: Possible contributions of citizen science for landslide hazard assessment. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 42, 295–300 (2018)
Kavzoglu, T.; Colkesen, I.; Sahin, E.K.: Machine learning techniques in landslide susceptibility mapping: a survey and a case study. Adv. Nat. Technol. Hazards Res. 50, 283–301 (2019). https://doi.org/10.1007/978-3-319-77377-3_13
Yalcin, A.; Reis, S.; Aydinoglu, A.C.; Yomralioglu, T.: A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA (2011). https://doi.org/10.1016/j.catena.2011.01.014
Peduzzi, P.: Landslides and vegetation cover in the 2005 North Pakistan earthquake: a GIS and statistical quantitative approach. Nat. Hazards Earth Syst. Sci. 10, 623–640 (2010). https://doi.org/10.5194/nhess-10-623-2010
Viet, T.T.; Lee, G.; Kim, M.: Shallow landslide assessment considering the ınfluence of vegetation cover. J. Korean Geoenviron. Soc. 17, 17–31 (2016). https://doi.org/10.14481/jkges.2016.17.4.17
Nefeslioglu, H.A.; Duman, T.Y.; Durmaz, S.: Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94, 401–418 (2008). https://doi.org/10.1016/j.geomorph.2006.10.036
Saleem, N.; Enamul Huq, M.; Twumasi, N.Y.D.; Javed, A.; Sajjad, A.: Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility mapping and landslide risk assessment: a review. ISPRS Int. J. Geo-Inf. 8, 545 (2019). https://doi.org/10.3390/ijgi8120545
Pachauri, A.K.; Gupta, P.V.; Chander, R.: Landslide zoning in a part of the Garhwal Himalayas. Environ. Geol. 36, 325–334 (1998). https://doi.org/10.1007/s002540050348
Tagil, S.; Jeff, J.: GIS-based automated landform classification and topographic, landcover and geologic attributes of landforms around the Yazoren Polje, Turkey. J. Appl. Sci. 8, 910–921 (2008)
Quinlan, J.T.: C4.5: Programs for Machine Learning. Elsevier, 2014
Park, S.; Kim, J.: Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl. Sci. (2019). https://doi.org/10.3390/app9050942
Pham, B.T.; Bui, D.T.; Dholakia, M.B.; Prakash, I.; Pham, H.V.; Mehmood, K.; Le, H.Q.: A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geom. Nat. Hazards Risk. 8, 649–671 (2017). https://doi.org/10.1080/19475705.2016.1255667
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Sahin, E.K.; Colkesen, I.; Kavzoglu, T.: A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int. 35, 341–363 (2020). https://doi.org/10.1080/10106049.2018.1516248
Kocaman, S.; Tavus, B.; Nefeslioglu, H.A.; Karakas, G.; Gokceoglu, C.: Evaluation of floods and landslides triggered by a meteorological catastrophe (Ordu, Turkey, August, 2018) using optical and radar data. Geofluids 2020, 8830661 (2020). https://doi.org/10.1155/2020/8830661
Kavzoglu, T.: Object-oriented random forest for high resolution land cover mapping using quickbird-2 ımagery. In: Samui, P.; Roy, S.S.; Balas, V.E. (Eds.) Handbook of Neural Computation, pp. 607–619. Elsevier Inc. (2017)
Chen, T.; Guestrin, C.: XGBoost: A Scalable Tree Boosting System. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, San Fr. CA, USA. 13–17 Augu, 785–794 (2016)
Sahin, E.K.: Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto Int. (2020). https://doi.org/10.1080/10106049.2020.1831623
Sahin, E.K.: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl. Sci. 2, 1–17 (2020). https://doi.org/10.1007/s42452-020-3060-1
Duan, T.; Avati, A.; Ding, D.Y.; Basu, S.; Ng, A.Y.; Schuler, A.: NGBoost: Natural gradient boosting for probabilistic prediction. In: Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020 (2019)
Chakraborty, D.; Elhegazy, H.; Elzarka, H.; Gutierrez, L.: A novel construction cost prediction model using hybrid natural and light gradient boosting. Adv. Eng. Inform. 46, 101201 (2020). https://doi.org/10.1016/j.aei.2020.101201
Peng, T.; Zhi, X.; Ji, Y.; Ji, L.; Tian, Y.: Prediction skill of extended range 2-m maximum air temperature probabilistic forecasts using machine learning post-processing methods. Atmosphere (Basel). 11, 1–17 (2020). https://doi.org/10.3390/ATMOS11080823
Dutta, S.: Revealing brain tumor using cross-validated ngboost classifier. Int. J. Mach. Learn. Netw. Collab. Eng. 4, 12–20 (2020). https://doi.org/10.30991/ijmlnce.2020v04i01.002
Lundberg, S.M.; Erion, G.; Chen, H.; DeGrave, A.; Prutkin, J.M.; Nair, B.; Katz, R.; Himmelfarb, J.; Bansal, N.; Lee, S.I.: Explainable AI for trees: From local explanations to global understanding. https://arxiv.org/abs/1905, 1–72 (2019)
Lundberg, S.M.; Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 4766–4775 (2017)
Štrumbelj, E.; Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647–665 (2014)
Menard, S.: Applied Logistic Regression Analysis: Sage University Series on Quantitative Applications in the Social Sciences. Sage Publication, Thousand Oaks (2002)
Park, S.; Choi, C.; Kim, B.; Kim, J.: Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ. Earth Sci. 68, 1443–1464 (2013). https://doi.org/10.1007/s12665-012-1842-5
Hong, H.; Liu, J.; Zhu, A.X.: Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci. Total Environ. 718, 137231 (2020). https://doi.org/10.1016/j.scitotenv.2020.137231
Wang, Y.; Fang, Z.; Wang, M.; Peng, L.; Hong, H.: Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput. Geosci. 138, 104445 (2020). https://doi.org/10.1016/j.cageo.2020.104445
Akgun, A.; Sezer, E.A.; Nefeslioglu, H.A.; Gokceoglu, C.; Pradhan, B.: An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput. Geosci. 38, 23–34 (2012). https://doi.org/10.1016/j.cageo.2011.04.012
Ayalew, L.; Yamagishi, H.: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65, 15–31 (2005). https://doi.org/10.1016/j.geomorph.2004.06.010
Tsangaratos, P.; Ilia, I.: Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. CATENA 145, 164–179 (2016). https://doi.org/10.1016/j.catena.2016.06.004
Kalantar, B.; Ueda, N.; Saeidi, V.; Ahmadi, K.; Halin, A.A.; Shabani, F.: Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens. 12, 1–23 (2020). https://doi.org/10.3390/rs12111737
Hu, X.; Mei, H.; Zhang, H.; Li, Y.; Li, M.: Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China. Nat. Hazards. 105, 1663–1689 (2021). https://doi.org/10.1007/s11069-020-04371-4
Arabameri, A.; Chandra Pal, S.; Rezaie, F.; Chakrabortty, R.; Saha, A.; Blaschke, T.; Di Napoli, M.; Ghorbanzadeh, O.; Thi Ngo, P.T.: Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto Int. (2021). https://doi.org/10.1080/10106049.2021.1892210
Daǧ, S.; Bulut, F.: An example for preparation of GIS-based landslide susceptibility maps: Çayeli (Rize, NE Türkiye). J. Geol. Eng. 36, 35–62 (2012)
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Kavzoglu, T., Teke, A. Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost). Arab J Sci Eng 47, 7367–7385 (2022). https://doi.org/10.1007/s13369-022-06560-8
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DOI: https://doi.org/10.1007/s13369-022-06560-8