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标题：Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
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作者：Phuong Thao Thi Ngo ; Mahdi Panahi ; Khabat Khosravi 等
期刊名称：Geoscience Frontiers
印刷版ISSN：1674-9871
出版年度：2021
卷号：12
期号：2
页码：505-519
DOI：10.1016/j.gsf.2020.06.013
语种：English
出版社：Elsevier
摘要：AbstractThe identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset. We calculated the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) for the quantitative evaluation of the landslide susceptibility maps using the testing dataset. Better performance in both the training and testing phases was provided by the RNN algorithm (AUC = 0.88) than by the CNN algorithm (AUC = 0.85). Finally, we calculated areas of susceptibility for each province and found that 6% and 14% of the land area of Iran is very highly and highly susceptible to future landslide events, respectively, with the highest susceptibility in Chaharmahal and Bakhtiari Province (33.8%). About 31% of cities of Iran are located in areas with high and very high landslide susceptibility. The results of the present study will be useful for the development of landslide hazard mitigation strategies.Graphical abstractDisplay OmittedHighlights•Landslide prone areas delineated based on CNN and RNN deep learning algorithms.•CNN model shows higher performance than RNN in landslide spatial prediction.•20% of the land areas of Iran are highly or very highly susceptible to landslide.•31% of cities are located in areas with high or very high landslide susceptibility.•Slope, geology, land use and distance from the faults are the most effective factors on landslide occurrences in Iran.
关键词：KeywordsenCNNRNNDeep learningLandslideIran