University of Oulu

Yunzhi Chen, Wei Chen, Omid Rahmati, Fatemeh Falah, Dominik Kulakowski, Saro Lee, Fatemeh Rezaie, Mahdi Panahi, Aref Bahmani, Hamid Darabi, Ali Torabi Haghighi & Huiyuan Bian (2021) Toward the development of deep-learning analyses for snow avalanche releases in Mountain regions, Geocarto International, http://dx.doi.org/10.1080/10106049.2021.1986578

Toward the development of deep-learning analyses for snow avalanche releases in Mountain regions

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Author: Chen, Yunzhi1; Chen, Wei1,2; Rahmati, Omid3;
Organizations: 1College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
2Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an 710021, China
3Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 6616936311, Iran
4Department of Watershed Management, Faculty of Natural Resources and Agriculture, Lorestan University, Lorestan, Iran
5Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610, USA
6Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro, Yuseong-gu, Daejeon, 34132, Republic of Korea
7Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Republic of Korea
8Division of Science Education, Kangwon National University, College of Education, # 4-301, Gangwondaehak-gil, Chuncheon-si, Gangwon do 24341, South Korea
9Natural Resources and Watershed Management Organization, Kurdistan Province, Sanandaj, Iran
10Water, Energy and Environmental Engineering Research unit, University of Oulu, P.O. Box 4300, FIN-90014 Oulu, Finland
11Water, Energy and Environmental Engineering Research unit, University of Oulu, P.O. Box 4300, FIN-90014 21 Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2021101250731
Language: English
Published: Informa, 2021
Publish Date: 2022-09-27
Description:

Abstract

Snow avalanches impose a considerable threat to infrastructure and human safety in snow bound mountain areas. Nevertheless, the spatial prediction of snow avalanches has received little research attention in many vulnerable parts of the world, particularly in developing countries. The present study investigates the applicability of a stand-alone convolutional neural network (CNN) model, as a deep-learning approach, along with two metaheuristic algorithms including grey wolf optimization (CNN-GWO) and imperialist competitive algorithm (CNN-ICA) in snow avalanche modeling in the Darvan watershed, Iran. The analysis was based on thirteen potential drivers of avalanche occurrence and an inventory map of previously documented avalanche occurrences. The efficiency of models’ performance was evaluated by Area Under the Receiver Operating Characteristic curve (AUC) and the Root Mean Square Error (RMSE). The CNN-ICA model yielded the highest accuracy in both training (AUC= 0.982, RMSE =0.067) and validation (AUC= 0.972, RMSE =0.125) steps, followed by the CNN-GWO model (AUC of 0.975 for training, RMSE of 0.18 for training, AUC of 0.968 for validation, RMSE of 0.157 for validation). However, the standalone CNN model showed lower goodness-of-fit (AUC= 0.864, RMSE =0.22) and predictive performance (AUC= 0.811, RMSE =0.330). The approach utilized in this study is broadly applicable for identifying areas where avalanche hazard is likely to be high and where mitigation measures or corresponding land use planning should be prioritized.

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Series: Geocarto international
ISSN: 1010-6049
ISSN-E: 1752-0762
ISSN-L: 1010-6049
Issue: Online first
DOI: 10.1080/10106049.2021.1986578
OADOI: https://oadoi.org/10.1080/10106049.2021.1986578
Type of Publication: B1 Journal article
Field of Science: 1171 Geosciences
Subjects:
GIS
Copyright information: © The Author(s). This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 27 Sep 2021, available online: http://www.tandfonline.com/10.1080/10106049.2021.1986578.