干旱区生态环境知识资源中心(Arid): New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed

Arid

DOI	10.3390/f10090743
	New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed
	Dieu Tien Bui 1,2; Shirzadi, Ataollah 3; Shahabi, Himan 4; Geertsema, Marten 5; Omidvar, Ebrahim 6; Clague, John J.7; Binh Thai Pham 8; Dou, Jie 9; Asl, Dawood Talebpour 4; Bin Ahmad, Baharin 10; Lee, Saro 11,12
通讯作者	Shahabi, Himan ; Lee, Saro
来源期刊	FORESTS
EISSN	1999-4907
出版年	2019
卷号	10 期号:9
英文摘要	We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but distance to road was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.
英文关键词	shallow landslide machine learning goodness-of-fit factor selection GIS Iran
类型	Article
语种	英语
国家	Vietnam ; Iran ; Canada ; Japan ; Malaysia ; South Korea
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000487978700085
WOS关键词	SUPPORT VECTOR MACHINE ; ARTIFICIAL-INTELLIGENCE APPROACH ; FUZZY INFERENCE SYSTEM ; ERROR PRUNING TREES ; NAIVE BAYES TREE ; LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; ROTATION FOREST ; DISCRIMINANT-ANALYSIS ; DECISION TREE
WOS类目	Forestry
WOS研究方向	Forestry
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/215704
作者单位	1.Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City, Vietnam; 2.Ton Duc Thang Univ, Fac Environm & Labor Safety, Ho Chi Minh City, Vietnam; 3.Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj 6617715175, Iran; 4.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj 6617715175, Iran; 5.Minist Forests Lands Nat Resource Operat & Rural, British Columbia, Prince George, BC V2L 1R5, Canada; 6.Univ Kashan, Fac Nat Resources & Earth Sci, Dept Rangeland & Watershed Management, Kashan 8731753153, Iran; 7.Simon Fraser Univ, Dept Earth Sci, 8888 Univ Dr Burnaby, Burnaby, BC V5A 1S6, Canada; 8.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 9.Nagaoka Univ Technol, Dept Civil & Environm Engn, 1603-1 Kami Tomioka, Nagaoka, Niigata 9402188, Japan; 10.UTM, Fac Built Environm & Surveying, Johor Baharu 81310, Malaysia; 11.Korea Inst Geosci & Mineral Resources KIGAM, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; 12.Korea Univ Sci & Technol, Dept Geophys Explorat, 217 Gajeong Ro, Daejeon 34113, South Korea
推荐引用方式 GB/T 7714	Dieu Tien Bui,Shirzadi, Ataollah,Shahabi, Himan,et al. New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed[J],2019,10(9).
APA	Dieu Tien Bui.,Shirzadi, Ataollah.,Shahabi, Himan.,Geertsema, Marten.,Omidvar, Ebrahim.,...&Lee, Saro.(2019).New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed.FORESTS,10(9).
MLA	Dieu Tien Bui,et al."New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed".FORESTS 10.9(2019).