Abstract
This study examines the use of snow avalanche susceptibility maps (SASMs) to identify areas prone to avalanches and develop measures to mitigate the risk in the Province of Sondrio, Italy. Various machine learning classifiers such as Random Forest, Gradient Boosting Machines, and AdaBoost, as well as newer classifiers like XGBoost, LightGBM, and NGBoost, were used with 17 conditioning factors and 1880 snow avalanche samples. The XGBoost classifier was found to have the best performance and McNemar’s test results indicated that certain classifier pairs, such as RF-AdaBoost, RF-XGBoost, and XGBoost-LightGBM, produced significant predictions while others did not. The XGBoost classifier found that 19.31% of Sondrio was very susceptible to avalanches. Instead of providing a global explanation of the classifier models, the study employs a local eXplainable Artificial Intelligence (XAI) approach called SHapley Additive eXplanations (SHAP) to give insight into how each conditioning factor contributes to the likelihood of snow avalanches. According to the SHAP values, the three most important factors in the XGBoost classifier model for determining the likelihood of snow avalanches are elevation, maximum temperature, and slope. The model shows that as elevation increases, the likelihood of avalanches also increases. On the other hand, a higher maximum temperature is found to decrease the likelihood of an avalanche. Slope is found to have a positive effect on the likelihood of an avalanche, meaning that steeper slopes increase the likelihood of an avalanche. This study also analyzes the avalanche susceptibility of ski resorts in the province and found that the majority of them are located in low and moderately susceptible areas, but some are in highly susceptible areas. The study used SHAP force plots to examine the local factors that contribute to the likelihood of avalanches in these specific ski resorts. The results show that ski resorts with elevations greater than 2000 m and slopes greater than 30 degrees, such as Livigno, Santa Caterina-Valfurva and Passo dello Stelvio, have a higher susceptibility to avalanches due to higher positive SHAP values. Conversely, ski resorts with elevations less than 2000 m and slopes less than 30 degrees, such as Aprica and Bormio, have a lower susceptibility to avalanches because of negative SHAP values. This study provides a valuable tool for creating new strategies to reduce the harm and damage caused by slow avalanches in the region.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abedi R, Costache R, Shafizadeh-Moghadam H, Pham QB (2021) Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int 1–18. https://doi.org/10.1080/10106049.2021.1920636
Akay H (2021) Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques. CATENA 206:105524. https://doi.org/10.1016/j.catena.2021.105524
Akay H (2022) Towards linking the Sustainable Development Goals and a novel-proposed Snow Avalanche susceptibility mapping. Water Resour Manage 1–18. https://doi.org/10.1007/s11269-022-03350-7
Akinci H (2022) Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques. J Afr Earth Sc 191:104535. https://doi.org/10.1016/j.jafrearsci.2022.104535
Al-Najjar HAH, Pradhan B, Beydoun G, Sarkar R, Park H-J, Alamri A (2022) A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset. https://doi.org/10.1016/j.gr.2022.08.004. Gondwana Research
Antolini F, Aiassa S, Barla M (2020) An Early Warning System for Debris Flows and Snow Avalanches (pp. 338–347). https://doi.org/10.1007/978-3-030-21359-6_36
Arabameri A, Pal C, Costache S, Saha R, Rezaie A, Danesh FSeyed, Pradhan A, Lee B, S., Hoang N-D (2021) Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics Nat Hazards Risk 12(1):469–498. https://doi.org/10.1080/19475705.2021.1880977
Arabameri A, Pradhan B, Bui DT (2020) Spatial modelling of gully erosion in the Ardib River Watershed using three statistical-based techniques. CATENA 190:104545. https://doi.org/10.1016/j.catena.2020.104545
Aydin HE, Iban MC (2022) Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations. https://doi.org/10.1007/s11069-022-05793-y. Natural Hazards
Barbolini M, Keylock CJ (2002) A new method for avalanche hazard mapping using a combination of statistical and deterministic models. Nat Hazards Earth Syst Sci 2(3/4):239–245. https://doi.org/10.5194/nhess-2-239-2002
Barbolini M, Pagliardi M, Ferro F, Corradeghini P (2009) Avalanche hazard mapping over large undocumented areas. Nat Hazards 56(2):451–464. https://doi.org/10.1007/s11069-009-9434-8
Barceló P, Monet M, Pérez J, Subercaseaux B (2020) Model Interpretability through the lens of Computational Complexity. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 15487–15498). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/file/b1adda14824f50ef24ff1c05bb66faf3-Paper.pdf
Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37(3):315–329. https://doi.org/10.1007/s11069-005-5182-6
Birkeland KW (2001) Spatial patterns of snow stability throughout a small mountain range. J Glaciol 47(157):176–186. https://doi.org/10.3189/172756501781832250
Brandolini P, Faccini F, Fratianni S, Freppaz M, Giardino M, Maggioni M, Perotti L, Romeo V (2017) Snow-Avalanche and climatic conditions in the Ligurian Ski Resorts (NW-Italy). Geografia Fisica e Dinamica Quaternaria 40(1):41–52. https://doi.org/10.4461/GFDQ2017.40.4
Breiman (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Brun E, Martin Ε, Simon V, Gendre C, Coleou C (1989) An Energy and Mass Model of Snow Cover suitable for operational Avalanche forecasting. J Glaciol 35(121):333–342. https://doi.org/10.3189/S0022143000009254
Bühler Y, Christen M, Kowalski J, Bartelt P (2011) Sensitivity of snow avalanche simulations to digital elevation model quality and resolution. Ann Glaciol 52(58):72–80. https://doi.org/10.3189/172756411797252121
Bühler Y, von Rickenbach D, Stoffel A, Margreth S, Stoffel L, Christen M (2018) Nat Hazards Earth Syst Sci 18(12):3235–3251. https://doi.org/10.5194/nhess-18-3235-2018. Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping
Campbell C, Conger S, Gould B, Haegeli P, Jamieson JB, Statham G (2016) Technical Aspects of Snow Avalanche Risk Management–Resources and Guidelines for Avalanche Practitioners in Canada. Revelstoke, BC, Canada, 15
Can R, Kocaman S, Gokceoglu C (2021) A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk dam. Turk Appl Sci 11(11):4993. https://doi.org/10.3390/app11114993
Canbek G, Sagiroglu S, Temizel TT, Baykal N (2017), October Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights. 2017 International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk.2017.8093539
Cantor SB, Kattan MW (2000) Determining the area under the ROC curve for a Binary Diagnostic Test. Med Decis Making 20(4):468–470. https://doi.org/10.1177/0272989X0002000410
Cetinkaya S, Kocaman S (2022) Snow Avalanche Susceptibility Mapping for Davos, Switzerland. The International Archives of Photogrammetry. Remote Sens Spat Inform Sci 43:1083–1090. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1083-2022
Chen T, Guestrin C (2016) XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939785
Chen Y, Chen W, Rahmati O, Falah F, Kulakowski D, Lee S, Rezaie F, Panahi M, Bahmani A, Darabi H, Torabi Haghighi A, Bian H (2021) Toward the development of deep learning analyses for snow avalanche releases in mountain regions. Geocarto Int 1–26. https://doi.org/10.1080/10106049.2021.1986578
Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1). https://doi.org/10.1186/s12864-019-6413-7
Choubin B, Borji M, Hosseini FS, Mosavi A, Dineva AA (2020) Mass wasting susceptibility assessment of snow avalanches using machine learning models. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-75476-w
Choubin B, Borji M, Mosavi A, Sajedi-Hosseini F, Singh VP, Shamshirband S (2019) Snow avalanche hazard prediction using machine learning methods. J Hydrol 577:123929. https://doi.org/10.1016/j.jhydrol.2019.123929
Darvishi Boloorani A, Neysani Samany N, Papi R, Soleimani M (2022) Dust source susceptibility mapping in Tigris and Euphrates basin using remotely sensed imagery. CATENA 209:105795. https://doi.org/10.1016/j.catena.2021.105795
De Reu J, Bourgeois J, Bats M, Zwertvaegher A, Gelorini V, De Smedt P, Chu W, Antrop M, De Maeyer P, Finke P, Van Meirvenne M, Verniers J, Crombé P (2013) Application of the topographic position index to heterogeneous landscapes. Geomorphology 186:39–49. https://doi.org/10.1016/j.geomorph.2012.12.015
Decaulne A, Saemundsson T (2006) Geomorphic evidence for present-day snow-avalanche and debris-flow impact in the icelandic westfjords. Geomorphology 80(1–2):80–93. https://doi.org/10.1016/j.geomorph.2005.09.007
Duan T, Avati A, Ding DY, Thai KK, Basu S, Ng AY, Schuler A (2019) October 8). NGBoost: natural gradient boosting for probabilistic prediction.ArXiv.Org. https://arxiv.org/abs/1910.03225
Durlević U, Valjarević A, Novković I, Ćurčić NB, Smiljić M, Morar C, …, Lukić T (2022) GIS-based spatial modeling of snow avalanches using analytic hierarchy process. A case study of the Šar mountains. Serbia Atmos 13(8):1229. https://doi.org/10.3390/atmos13081229
Elkin C, Gutiérrez AG, Leuzinger S, Manusch C, Temperli C, Rasche L, Bugmann H (2013) A 2°C warmer world is not safe for ecosystem services in the european Alps. Glob Change Biol 19(6):1827–1840. https://doi.org/10.1111/gcb.12156
Fang, Z., Wang, Y., Peng, L., & Hong, H. (2020). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 139, 104470. https://doi.org/10.1016/j.cageo.2020.104470
Fazzini M, Cordeschi M, Carabella C, Paglia G, Esposito G, Miccadei E (2021) Snow Avalanche Assessment in Mass Movement-Prone Areas: results from Climate Extremization in Relationship with Environmental Risk reduction in the Prati di Tivo Area (Gran Sasso Massif, Central Italy). Land 10(11):1176. https://doi.org/10.3390/land10111176
Fick SE, Hijmans RJ (2017) WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315. https://doi.org/10.1002/joc.5086
Fischer J-T, Kowalski J, Pudasaini SP (2012) Topographic curvature effects in applied avalanche modeling. Cold Reg Sci Technol 74–75:21–30. https://doi.org/10.1016/j.coldregions.2012.01.005
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139. https://doi.org/10.1006/jcss.1997.1504
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. The Annals of Statistics 29(5). https://doi.org/10.1214/aos/1013203451
Gauthier F, Germain D, Hétu B (2017) Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: Northern Gaspésie. Québec Can Nat Hazards 89(1):201–232. https://doi.org/10.1007/s11069-017-2959-3
Ghinoi A, Chung C-J (2005) STARTER: a statistical GIS-based model for the prediction of snow avalanche susceptibility using terrain features—application to Alta val Badia, italian dolomites. Geomorphology 66(1–4):305–325. https://doi.org/10.1016/j.geomorph.2004.09.018
Giacona F, Eckert N, Corona C, Mainieri R, Morin S, Stoffel M, Martin B, Naaim M (2021) Upslope migration of snow avalanches in a warming climate. Proceedings of the National Academy of Sciences, 118(44). https://doi.org/10.1073/pnas.2107306118
Gruber, U., & Bartelt, P. (2007). Snow avalanche hazard modelling of large areas using shallow water numerical methods and GIS. Environmental Modelling & Software, 22(10),1472–1481. https://doi.org/10.1016/j.envsoft.2007.01.001
Gruber U, Margreth S (2001) Winter 1999: a valuable test of the avalanche-hazard mapping procedure in Switzerland. Ann Glaciol 32:328–332. https://doi.org/10.3189/172756401781819238
Haeberli W, Alean J-C, Müller P, Funk M (1989) Assessing risks from glacier hazards in high mountain regions: some experiences in the swiss alps. Ann Glaciol 13:96–102. https://doi.org/10.3189/s0260305500007709
Höller P (2007) Avalanche hazards and mitigation in Austria: a review. Nat Hazards 43(1):81–101. https://doi.org/10.1007/s11069-007-9109-2
Hong H, Miao Y, Liu J, Zhu A-X (2019) CATENA 176:45–64. https://doi.org/10.1016/j.catena.2018.12.035. Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping
Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158. https://doi.org/10.1109/34.574797
Jena R, Pradhan B, Gite S, Alamri A, Park H-J (2022) A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model. https://doi.org/10.1016/j.gr.2022.10.003. Gondwana Research
Iban MC, Sekertekin A (2022) Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: a case study of Adana and Mersin provinces. Turk Ecol Inf 69:101647. https://doi.org/10.1016/j.ecoinf.2022.101647
Kavzoglu T, Kutlug Sahin E, Colkesen I (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol 192:101–112. https://doi.org/10.1016/j.enggeo.2015.04.004
Kavzoglu T, Teke A (2022a) 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(6):7367–7385. https://doi.org/10.1007/s13369-022-06560-8
Kavzoglu T, Teke A (2022b) Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost). Bull Eng Geol Environ 81(5). https://doi.org/10.1007/s10064-022-02708-w
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T (2017) LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 31st Conference on Neural Information Processing Systems (NIPS 2017)
Kincal C, Kayhan H (2022) A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Türkiye). Appl Sci 12(18):9029. https://doi.org/10.3390/app12189029
Kumar S, Srivastava PK, Snehmani (2016) GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region. Indian Himalaya Geocarto International 32(11):1254–1267. https://doi.org/10.1080/10106049.2016.1206626
Kumar S, Srivastava PK, Snehmani, Bhatiya S (2019) Geospatial probabilistic modelling for release area mapping of snow avalanches. Cold Reg Sci Technol 165:102813. https://doi.org/10.1016/j.coldregions.2019.102813
Laute K, Beylich AA (2018) Potential effects of climate change on future snow avalanche activity in western Norway deduced from meteorological data. Geogr Annaler: Ser Phys Geogr 100(2):163–184. https://doi.org/10.1080/04353676.2018.1425622
Lee S, Bae JH, Hong J, Yang D, Panagos P, Borrelli P, Yang JE, Kim J, Lim KJ (2022) Estimation of rainfall erosivity factor in Italy and Switzerland using bayesian optimization based machine learning models. CATENA 211:105957. https://doi.org/10.1016/j.catena.2021.105957
Louchet F (2021) Snow avalanches: beliefs, facts, and science. Oxford University Press, USA
Lundberg S, Lee S-I (2017) A unified approach to interpreting model predictions.ArXiv.Org. https://arxiv.org/abs/1705.07874
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56–67. https://doi.org/10.1038/s42256-019-0138-9
Maggioni M, Godone D, Frigo B, Freppaz M (2019) Snow gliding and glide-snow avalanches: recent outcomes from two experimental test sites in Aosta Valley (northwestern italian Alps). Nat Hazards Earth Syst Sci 19(11):2667–2676. https://doi.org/10.5194/nhess-19-2667-2019
Mainieri R, Favillier A, Lopez-Saez J, Eckert N, Zgheib T, Morel P, Saulnier M, Peiry J-L, Stoffel M, Corona C (2020) Impacts of land-cover changes on snow avalanche activity in the French Alps. Anthropocene 30:100244. https://doi.org/10.1016/j.ancene.2020.100244
Malczewski J (1999) GIS and multicriteria decision analysis. John Wiley & Sons
McClung D, Schaerer PA (2006) The avalanche handbook. The Mountaineers Books
McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2):153–157. https://doi.org/10.1007/bf02295996
Moghaddam DD, Rahmati O, Panahi M, Tiefenbacher J, Darabi H, Haghizadeh A, Haghighi AT, Nalivan OA, Bui T, D (2020) The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. CATENA 187:104421. https://doi.org/10.1016/j.catena.2019.104421
Mohammadifar A, Gholami H, Comino JR, Collins AL (2021) CATENA 200:105178. https://doi.org/10.1016/j.catena.2021.105178. Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory
Mosavi A, Shirzadi A, Choubin B, Taromideh F, Hosseini FS, Borji M, Shahabi H, Salvati A, Dineva AA (2020) Towards an ensemble machine learning model of random subspace based functional tree classifier for snow avalanche susceptibility mapping. IEEE Access 8:145968–145983. https://doi.org/10.1109/access.2020.3014816
Nasery S, Kalkan K (2021) Snow avalanche risk mapping using GIS-based multi-criteria decision analysis: the case of Van, Turkey. Arab J Geosci 14(9). https://doi.org/10.1007/s12517-021-07112-4
Panahi M, Rahmati O, Rezaie F, Lee S, Mohammadi F, Conoscenti C (2022) Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates. CATENA 208:105779. https://doi.org/10.1016/j.catena.2021.105779
Paul BK (2020) Natural hazards and disasters: from Avalanches and Climate Change to Water Spouts and Wildfires [2 volumes]. ABC-CLIO
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Peitzsch EH, Hendrikx J, Fagre DB (2015) Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model. Cold Reg Sci Technol 120:237–250. https://doi.org/10.1016/j.coldregions.2015.08.002
Peitzsch EH, Pederson GT, Birkeland KW, Hendrikx J, Fagre DB (2021) Climate drivers of large magnitude snow avalanche years in the U.S. northern Rocky Mountains. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-89547-z
Pfiffner OA (2014) Geology of the Alps. John Wiley & Sons, Chicago
Pham BT, Bui DT, Dholakia MB, Prakash I, Pham HV, Mehmood K, Le HQ (2016) 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. Geomatics, Natural Hazards and Risk, 8(2), 649–671. https://doi.org/10.1080/19475705.2016.1255667
Pham BT, Bui T, Prakash D, I., Dholakia MB (2017) Hybrid integration of Multilayer Perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63. https://doi.org/10.1016/j.catena.2016.09.007
Pham K, Kim D, Park S, Choi H (2021) Ensemble learning-based classification models for slope stability analysis. CATENA 196:104886. https://doi.org/10.1016/j.catena.2020.104886
Pietri C (1993) Rénovation de la carte de localisation probable des Avalanches. Revue de Géographie Alpine 81(1):85–97. https://doi.org/10.3406/rga.1993.3697
Pudasaini SP, Hutter K (2007) Avalanche dynamics: Dynamics of rapid flows of dense granular avalanches. Springer Science & Business Media
Rahmati O, Ghorbanzadeh O, Teimurian T, Mohammadi F, Tiefenbacher JP, Falah F, Pirasteh S, Ngo P-TT, Bui DT (2019) Spatial modeling of snow avalanche using machine learning models and geo-environmental factors: comparison of effectiveness in two mountain regions. Remote Sens 11(24):2995. https://doi.org/10.3390/rs11242995
Rangwala I, Miller JR (2012) Climate change in mountains: a review of elevation-dependent warming and its possible causes. Clim Change 114(3–4):527–547. https://doi.org/10.1007/s10584-012-0419-3
Rheinberger CM, Bründl M, Rhyner J (2009) Dealing with the white death: Avalanche risk management for traffic routes. Risk Analysis: An International Journal 29(1):76–94. https://doi.org/10.1111/j.1539-6924.2008.01127.x
Różycka M, Migoń P, Michniewicz A (2017) Topographic Wetness Index and Terrain Ruggedness Index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Z Für Geomorphologie Supplementary Issues 61(2):61–80. https://doi.org/10.1127/zfg_suppl/2016/0328
Saber M, Boulmaiz T, Guermoui M, Abdrabo KI, Kantoush SA, Sumi T, Boutaghane H, Nohara D, Mabrouk E (2021) Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction. Geocarto Int 1–26. https://doi.org/10.1080/10106049.2021.1974959
Sahin EK (2020) Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Appl Sci 2(7). https://doi.org/10.1007/s42452-020-3060-1
Schneebeli M, Bebi P (2004) Hydrology | snow and avalanche control. In Encyclopedia of Forest Sciences (pp. 397–402). Elsevier. https://doi.org/10.1016/b0-12-145160-7/00271-4
Schweizer J, Jamieson B, J., Schneebeli M (2003) Snow avalanche formation. Rev Geophys 41(4). https://doi.org/10.1029/2002RG000123
Selcuk L (2013) An avalanche hazard model for Bitlis Province, Turkey, using GIS based multicriteria decision analysis. Turkish J Earth Sci 22(4):523–535. https://doi.org/10.3906/yer-1201-10
Shapley LS (1953) Stochastic games. Proceedings of the National Academy of Sciences, 39(10), 1095–1100. https://doi.org/10.1073/pnas.39.10.1095
Silla CN Jr, Freitas AA (2010) A survey of hierarchical classification across different application domains. Data Min Knowl Disc 22(1–2):31–72. https://doi.org/10.1007/s10618-010-0175-9
Singh DK, Mishra VD, Gusain HS, Gupta N, Singh AK (2019) Geo-spatial modeling for automated demarcation of snow avalanche hazard areas using landsat-8 satellite ımages and ın situ data. J Indian Soc Remote Sens 47(3):513–526. https://doi.org/10.1007/s12524-018-00936-w
Součková M, Juras R, Dytrt K, Moravec V, Blöcher JR, Hanel M (2022) What weather variables are important for wet and slab avalanches under a changing climate in low altitude mountain range in Czechia? Nat Hazards Earth Syst Sci Discuss 1–35. https://doi.org/10.5194/nhess-22-3501-2022
Stoffel M, Corona C (2018) Future winters glimpsed in the Alps. Nat Geosci 11(7):458–460. https://doi.org/10.1038/s41561-018-0177-6
Tiwari A, G., A., Vishwakarma BD (2021) Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India. Sci Total Environ 794:148738. https://doi.org/10.1016/j.scitotenv.2021.148738
Ulivieri G, Marchetti E, Ripepe M, Chiambretti I, de Rosa G, Segor V (2011) Monitoring snow avalanches in Northwestern Italian Alps using an infrasound array. Cold Reg Sci Technol 69(2–3):177–183. https://doi.org/10.1016/j.coldregions.2011.09.006
Varol N (2022) Avalanche susceptibility mapping with the use of frequency ratio, fuzzy and classical analytical hierarchy process for Uzungol area, Turkey. Cold Reg Sci Technol 194:103439. https://doi.org/10.1016/j.coldregions.2021.103439
Vega García M, Aznarte JL (2020) Shapley additive explanations for NO2 forecasting. Ecol Inf 56:101039. https://doi.org/10.1016/j.ecoinf.2019.101039
Wen H, Wu X, Liao X, Wang D, Huang K, Wünnemann B (2022) Application of machine learning methods for snow avalanche susceptibility mapping in the Parlung Tsangpo catchment, southeastern Qinghai-Tibet Plateau. Cold Reg Sci Technol 198:103535. https://doi.org/10.1016/j.coldregions.2022.103535
Wu Y, Ke Y, Chen Z, Liang S, Zhao H, Hong H (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. CATENA 187:104396. https://doi.org/10.1016/j.catena.2019.104396
Yang J, He Q, Liu Y (2022) Winter–spring prediction of snow avalanche susceptibility using optimisation multi-source heterogeneous factors in the western Tianshan Mountains, China. Remote Sens 14(6):1340. https://doi.org/10.3390/rs14061340
Yariyan P, Avand M, Abbaspour RA, Karami M, Tiefenbacher JP (2020) GIS-based spatial modeling of snow avalanches using four novel ensemble models. Sci Total Environ 745:141008. https://doi.org/10.1016/j.scitotenv.2020.141008
Yariyan P, Omidvar E, Karami M, Cerdà A, Pham QB, Tiefenbacher JP (2022) Evaluating novel hybrid models based on GIS for snow avalanche susceptibility mapping: a comparative study. Cold Reg Sci Technol 194:103453. https://doi.org/10.1016/j.coldregions.2021.103453
Yariyan P, Omidvar E, Minaei F, Abbaspour A, R., Tiefenbacher JP (2021) An optimization on machine learning algorithms for mapping snow avalanche susceptibility. Nat Hazards 111(1):79–114. https://doi.org/10.1007/s11069-021-05045-5
Yi Y, Zhang Z, Zhang W, Jia H, Zhang J (2020) Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region. CATENA 195:104851. https://doi.org/10.1016/j.catena.2020.104851
Yousefi S, Pourghasemi HR, Emami SN, Pouyan S, Eskandari S, Tiefenbacher JP (2020) A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci Rep 10(1). https://doi.org/10.1038/s41598-020-69233-2
Zhang, H., Song, Y., Xu, S., He, Y., Li, Z., Yu, X., Liang, Y., Wu, W., & Wang, Y.(2022). Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir,China. Computers & Geosciences, 158, 104966. https://doi.org/10.1016/j.cageo.2021.104966
Zhou X, Wen H, Li Z, Zhang H, Zhang W (2022) An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost. Geocarto International, pp 1–32
Acknowledgements
We would like to express our sincere appreciation to the handling editor and anonymous reviewers for their valuable time, expertise and effort in reviewing our manuscript. Their insightful comments and constructive feedback have greatly contributed to improving the quality and content of our work. We would like to extend our sincerest gratitude to the Region of Lombardy for making their snow avalanche data sets readily available for free through their geoportal. This valuable information greatly aided in our research and efforts to better understand and predict snow avalanches. Thank you for your support and commitment to providing open access to important data.
Author information
Authors and Affiliations
Contributions
Conceptualization: M.C.I. and S.S.B; Methodology: M.C.I and S.S.B; Data Curation: S.S.B; Formal analysis: M.C.I; Validation: M.C.I; Visualization: M.C.I and S.S.B. Writing - Original Draft: M.C.I and S.S.B. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.The authors have no relevant financial or non-financial interests to disclose
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
IBAN, M.C., BILGILIOGLU, S.S. Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach. Stoch Environ Res Risk Assess 37, 2243–2270 (2023). https://doi.org/10.1007/s00477-023-02392-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-023-02392-6