Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The 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 land...

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Veröffentlicht in:Di xue qian yuan. 2021-03, Vol.12 (2), p.505-519
Hauptverfasser: Thi Ngo, Phuong Thao, Panahi, Mahdi, Khosravi, Khabat, Ghorbanzadeh, Omid, Kariminejad, Narges, Cerda, Artemi, Lee, Saro
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container_issue 2
container_start_page 505
container_title Di xue qian yuan.
container_volume 12
creator Thi Ngo, Phuong Thao
Panahi, Mahdi
Khosravi, Khabat
Ghorbanzadeh, Omid
Kariminejad, Narges
Cerda, Artemi
Lee, Saro
description The 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. [Display omitted] •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.
doi_str_mv 10.1016/j.gsf.2020.06.013
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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. [Display omitted] •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.</description><identifier>ISSN: 1674-9871</identifier><identifier>EISSN: 2588-9192</identifier><identifier>DOI: 10.1016/j.gsf.2020.06.013</identifier><language>eng</language><publisher>Oxford: Elsevier B.V</publisher><subject>Algorithms ; Artificial neural networks ; CNN ; Curvature ; Datasets ; Deep learning ; Geological hazards ; Hazard assessment ; Hazard mitigation ; Iran ; Landslide ; Landslides ; Landslides &amp; mudslides ; Machine learning ; Mapping ; Mathematical analysis ; Neural networks ; Rainfall ; Recurrent neural networks ; RNN ; Training</subject><ispartof>Di xue qian yuan., 2021-03, Vol.12 (2), p.505-519</ispartof><rights>2020 China University of Geosciences (Beijing) and Peking University</rights><rights>Copyright Elsevier Science Ltd. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a489t-a30918430410e3a65a365725b2a118baa0cad93f5ad302ffd4f806f570bebe523</citedby><cites>FETCH-LOGICAL-a489t-a30918430410e3a65a365725b2a118baa0cad93f5ad302ffd4f806f570bebe523</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/dxqy-e/dxqy-e.jpg</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1674987120301687$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Thi Ngo, Phuong Thao</creatorcontrib><creatorcontrib>Panahi, Mahdi</creatorcontrib><creatorcontrib>Khosravi, Khabat</creatorcontrib><creatorcontrib>Ghorbanzadeh, Omid</creatorcontrib><creatorcontrib>Kariminejad, Narges</creatorcontrib><creatorcontrib>Cerda, Artemi</creatorcontrib><creatorcontrib>Lee, Saro</creatorcontrib><title>Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran</title><title>Di xue qian yuan.</title><description>The 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. [Display omitted] •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.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>CNN</subject><subject>Curvature</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Geological hazards</subject><subject>Hazard assessment</subject><subject>Hazard mitigation</subject><subject>Iran</subject><subject>Landslide</subject><subject>Landslides</subject><subject>Landslides &amp; mudslides</subject><subject>Machine learning</subject><subject>Mapping</subject><subject>Mathematical analysis</subject><subject>Neural networks</subject><subject>Rainfall</subject><subject>Recurrent neural networks</subject><subject>RNN</subject><subject>Training</subject><issn>1674-9871</issn><issn>2588-9192</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kMFq3DAQhkVpoEuSB8hN0ENPdkaSZcn0VEKaBAK9tGcxtqStFq3sSN60-_bVdgu9ZS5z-f6fmY-QGwYtA9bf7tpt8S0HDi30LTDxjmy41LoZ2MDfkw3rVdcMWrEP5LqUHdRRSisFGzLev2I84BrmRGdPrXMLjQ5zCmlLMW7nHNaf-0L9nGn6i2GkZcLoaMRkSwzW0XIok1vWMIYY1iPd47Kc4rXvKWO6IhceY3HX__Yl-fH1_vvdY_P87eHp7stzg50e1gYFDEx3AjoGTmAvUfRScTlyZEyPiDChHYSXaAVw723nNfReKhjd6CQXl-TTufcXJo9pa3bzIddzi7G_X47GVT-sKoIT-fFMLnl-Obiy_ke55ForyUBUip2pKc-lZOfNksMe89EwMCfvZmeqd3PybqA31XvNfD5nXH30NbhsyhRcmpwN2U2rsXN4I_0Hm8mK_g</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Thi Ngo, Phuong Thao</creator><creator>Panahi, Mahdi</creator><creator>Khosravi, Khabat</creator><creator>Ghorbanzadeh, Omid</creator><creator>Kariminejad, Narges</creator><creator>Cerda, Artemi</creator><creator>Lee, Saro</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><general>Geoscience Platform Division,Korea Institute of Geoscience and Mineral Resources (KIGAM),124,Gwahak-ro Yuseong-gu,Daejeon,34132,Republic of Korea%Department of Watershed Management Engineering,Sari Agricultural Science and Natural Resources University,Sari,Iran%Department of Geoinformatics–Z_GIS,University of Salzburg,Salzburg,5020,Austria%Department of Watershed and Arid Zone Management,Gorgan University of Agricultural Sciences and Natural Resources,Gorgan,49189-434,Iran%Soil Erosion and Desertification Research Group,Department of Geography,University of Valencia,Valencia,Spain%Geoscience Platform Division,Korea Institute of Geoscience and Mineral Resources (KIGAM),124,Gwahak-ro Yuseong-gu,Daejeon,34132,Republic of Korea</general><general>Institute of Research and Development,Duy Tan University,Da Nang,550000,Viet Nam%Division of Science Education,Kangwon National University,Chuncheon-si,Gangwon-do,24341,Republic of Korea</general><general>Department of Geophysical Exploration,Korea University of Science and Technology,217 Gajeong-ro Yuseong-gu,Daejeon,34113,Republic of Korea</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20210301</creationdate><title>Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran</title><author>Thi Ngo, Phuong Thao ; 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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. [Display omitted] •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.</abstract><cop>Oxford</cop><pub>Elsevier B.V</pub><doi>10.1016/j.gsf.2020.06.013</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record>
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subjects Algorithms
Artificial neural networks
CNN
Curvature
Datasets
Deep learning
Geological hazards
Hazard assessment
Hazard mitigation
Iran
Landslide
Landslides
Landslides & mudslides
Machine learning
Mapping
Mathematical analysis
Neural networks
Rainfall
Recurrent neural networks
RNN
Training
title Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
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