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9. A cautionary note for rock avalanche field investigation: recent sequential and overlapping landslides in British Columbia

Author

Geertsema, Marten and Bevington, Alexandre

Subjects
Rockslides -- Models, Artificial satellites in remote sensing -- Usage, Earth sciences
Abstract

Large rock avalanches on glaciers are an annual occurrence in the mountains of western North America. Following an event, landslide investigators may strive to quickly arrive on site to assess the deposit. Satellite remote sensing imagery demonstrates that caution is warranted for on-site field assessments. We combine Landsat, Sentinel-1 (radar), Sentinel-2, and Planet imagery to reconstruct the events of four recent overlapping rock avalanche deposits in British Columbia. In our examples, substantial rock avalanches are closely followed (days-months) and buried by much larger landslides. We suggest that landslide investigators exercise caution when assessing fresh rock avalanche deposits in the field. Key words: landslides, rock avalanche, field investigation, glacier, British Columbia. Les grandes avalanches de rocheuses sur les glaciers sont une occurrence annuelle dans les montagnes de l'ouest de l'Amerique du Nord. A la suite d'un evenement, les enqueteurs peuvent s'efforcer d'arriver rapidement sur place pour evaluer le glissement. L'imagerie par teledetection par satellite demontre qu'une prudence est justifiee pour les evaluations sur place sur le terrain. Nous combinons les images Landsat, Sentinel-1 (radar), Sentinel-2 et Planet pour reconstituer les evenements de quatre recentes de double avalanches rocheuses en Colombie-Britannique. Dans nos exemples, les avalanches de rocheuses initiales sont suivies de pres (jours-mois) et ensevelies par des glissements de terrain beaucoup plus importants. Nous suggerons aux enqueteurs de glissement de terrain de faire preuve de prudence lorsqu'ils evaluent les avalanches de roches fraiches sur le terrain. Mots-cles: glissements, avalanche rocheuse, etude de terrain, glacier, Colombie-Britannique., Introduction Large landslides in western North America appear to be on the increase (Evans and Clague 1994; Geertsema et al. 2006; Huggel et al. 2012; Cloutier et al. 2017; Coe [...]

Published
2021
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15. Contributors

Author

Allison, Ian, primary, Alzate, Bernardo Pulgarín, additional, Amann, Florian, additional, Arenson (U.), Lukas, additional, Bartelt, Perry, additional, Bebiolka, Anke, additional, Bodin, Xavier, additional, Bolch, Tobias, additional, Brandefelt, Jenny, additional, Bründl, Michael, additional, Cáceres Correa, Bolívar E., additional, Callaghan, Terry V., additional, Carey, Mark, additional, Christiansen, Hanne H., additional, Clague, John J., additional, Cohen, Denis, additional, Colgan, William, additional, Cook, Simon, additional, Craw Burns, Patty A., additional, Delaloye, Reynald, additional, Delaney, Keith B., additional, Deline, Philip, additional, Espizua, Lydia, additional, Evans, Stephen G., additional, Ewen, Tracy, additional, Failletaz, Jérôme, additional, Fischer, Luzia, additional, Fischer, Urs H., additional, Fowler, Captain David, additional, Gärtner-Roer, Isabelle, additional, Geertsema, Marten, additional, Giardino, Marco, additional, Gilbert, Adrien, additional, Granados, Hugo Delgado, additional, Gruber, Stephan, additional, Haeberli, Wilfried, additional, Harper, Joel, additional, Harrison, Will D., additional, Hasler, Andreas, additional, Heckmann, Tobias, additional, Hirschorn, Sarah, additional, Huggel, Christian, additional, Huss, Matthias, additional, Jenicek, Michal, additional, Jensen, Mark, additional, Johansson, Margareta, additional, Kääb, Andreas, additional, Kennell, Laura, additional, King, Matt, additional, Kirkbride, Martin, additional, Korup, Oliver, additional, Krautblatter, Michael, additional, Kubat, Ivana, additional, Lai, Alexandre W., additional, Liakka, Johan, additional, Magnin, Florence, additional, Margreth, Stefan, additional, Marshall, Becca, additional, Marshall, Hans Peter, additional, McColl, Samuel, additional, McDowell, Graham, additional, Miranda, Patricia Julio, additional, Moore, Jeffrey, additional, Mothes, Patricia, additional, Moulton, Holly, additional, Näslund, Jens-Ove, additional, Normani, Stefano, additional, Nosenko, Gennady A., additional, Núñez, Gerardo Carrasco, additional, O’Connor, Jim E., additional, Ohmura, Atsumu, additional, Osipova, Galina B., additional, Paul, Frank, additional, Portocarrero, César, additional, Provant, Zachary, additional, Ramos, Jorge Cortés, additional, Rana, Nahyan M., additional, Ravanel, Ludovic, additional, Reynolds, John M., additional, Roa, Hugo Moreno, additional, Sayed, Mohamed, additional, Schoeneich, Philippe, additional, Schweizer, Jürg, additional, Seibert, Jan, additional, Streletskiy, Dmitry, additional, Stück, Heidrun, additional, Swift, Darrel A., additional, Truffer, Martin, additional, van Herwijnen, Alec, additional, Vicuña, Luis, additional, Vieli, Andreas, additional, Viviroli, Daniel, additional, Weber, Samuel, additional, Weitkamp, Axel, additional, and Whiteman, Colin, additional

Published
2021
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16. Ice loss from glaciers and permafrost and related slope instability in high-mountain regions

Author

Deline, Philip, primary, Gruber, Stephan, additional, Amann, Florian, additional, Bodin, Xavier, additional, Delaloye, Reynald, additional, Failletaz, Jérôme, additional, Fischer, Luzia, additional, Geertsema, Marten, additional, Giardino, Marco, additional, Hasler, Andreas, additional, Kirkbride, Martin, additional, Krautblatter, Michael, additional, Magnin, Florence, additional, McColl, Samuel, additional, Ravanel, Ludovic, additional, Schoeneich, Philippe, additional, and Weber, Samuel, additional

Published
2021
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18. Glacier Environments

Author

Menounos, B., Bevington, Alexandre, Geertsema, Marten, Bobrowsky, Peter T., editor, and Marker, Brian, editor

Published
2018
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20. Mountain Environments

Author

Bevington, Alexandre, Brideau, Marc-André, Geertsema, Marten, Bobrowsky, Peter T., editor, and Marker, Brian, editor

Published
2018
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27. Quaternary geology of part of the McLeod Lake map area (NTS 093J), central British Columbia: lithostratigraphy, glacial history, and chronology

Author

Sacco, David A., Ward, Brent C., Lian, Olav B., Maynard, Denny E., and Geertsema, Marten

Subjects
Glaciers -- Natural history, Landforms -- Natural history, Earth sciences
Abstract

Abstract: The coalescence and subsequent divergence of glaciers near the McLeod Lake map area during the last (Fraser) glaciation are recorded by landforms, ice-flow indicators, and the distribution of sediments. [...]

Published
2017
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31. Quick Clay

Author

Geertsema, Marten, Bobrowsky, Peter T., editor, and Marker, Brian, editor

Published
2018
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33. The 2015 landslide and tsunami in Taan Fiord, Alaska

Author

Higman, Bretwood, Shugar, Dan H., Stark, Colin P., Ekström, Göran, Koppes, Michele N., Lynett, Patrick, Dufresne, Anja, Haeussler, Peter J., Geertsema, Marten, Gulick, Sean, Mattox, Andrew, Venditti, Jeremy G., Walton, Maureen A. L., McCall, Naoma, Mckittrick, Erin, MacInnes, Breanyn, Bilderback, Eric L., Tang, Hui, Willis, Michael J., Richmond, Bruce, Reece, Robert S., Larsen, Chris, Olson, Bjorn, Capra, James, Ayca, Aykut, Bloom, Colin, Williams, Haley, Bonno, Doug, Weiss, Robert, Keen, Adam, Skanavis, Vassilios, and Loso, Michael

Published
2018
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38. Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

Author

Ghasemian, Bahareh, Shahabi, Himan, Shirzadi, Ataollah, Al-Ansari, Nadhir, Jaafari, Abolfazl, Geertsema, Marten, Melesse, Assefa M., Singh, Sushant K., and Ahmad, Anuar

Subjects
spatial modeling, Physical Geography, Naturgeografi, rotation forest, decision tree, landslide susceptibility, Iran, GIS, random forest, General Environmental Science
Abstract

Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world. Validerad;2022;Nivå 2;2022-06-15 (joosat);Funder: University of Kurdistan, Iran (00-9-34027-4469)

Published
2022
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48. A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

Author

Ghasemian, Bahareh, Shahabi, Himan, Shirzadi, Ataollah, Al-Ansari, Nadhir, Jaafari, Abolfazl, Kress, Victoria R., Geertsema, Marten, Renoud, Somayeh, and Ahmad, Anuar

Subjects
extreme learning machine, deep belief network, Geoteknik, genetic algorithm, landslide susceptibility, Iran, Geotechnical Engineering, GIS
Abstract

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping. Validerad;2022;Nivå 2;2022-02-18 (sofila);Funder: University of Kurdistan, Iran (grant no. 11-99-4469)

Published
2022

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