Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

dc.contributor.authorRahmati, Omid
dc.contributor.authorGhorbanzadeh, Omid
dc.contributor.authorTeimurian, Teimur
dc.contributor.authorMohammadi, Farnoush
dc.contributor.authorTiefenbacher, John
dc.contributor.authorFalah, Fatemeh
dc.contributor.authorPirasteh, Saied
dc.contributor.authorNgo, Phuong-Thao Thi
dc.contributor.authorBui, Dieu Tien
dc.description.abstractAlthough snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.
dc.description.departmentGeography and Environmental Studies
dc.format.extent26 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationRahmati, O., Ghorbanzadeh, O., Teimurian, T., Mohammadi, F., Tiefenbacher, J. P., Falah, F., Pirasteh, S., Ngo, P-T T., Bui, D. T. (2019). Spatial modeling of snow avalanche using machine learning models and geo-environmental factors: Comparison of effectiveness in two mountain regions. Remote Sensing, 11(24):2995.
dc.publisherMultidisciplinary Digital Publishing Institute
dc.rights.holder© 2019 The Authors.
dc.rights.licenseThis work is licensed under a Creative Commons Attribution 4.0 International License.
dc.sourceRemote Sensing, 2019, Vol. 11, No. 24, Article 2995.
dc.subjectartificial intelligence
dc.subjectgeomorphometric analysis
dc.subjectsnow avalanche
dc.subjectGeography and Environmental Studies
dc.titleSpatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions


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