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DOI	10.3390/f11040421
	Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran
	Viet-Ha Nhu 1,2; Shirzadi, Ataollah 3; Shahabi, Himan 4,5; Chen, Wei 6,7; Clague, John J.8; Geertsema, Marten 9; Jaafari, Abolfazl 10; Avand, Mohammadtaghi 11; Miraki, Shaghayegh 12; Asl, Davood Talebpour 4; Binh Thai Pham 13; Bin Ahmad, Baharin 14; Lee, Saro 15,16
通讯作者	Binh Thai Pham ; Lee, Saro
来源期刊	FORESTS
EISSN	1999-4907
出版年	2020
卷号	11 期号:4
英文摘要	We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers-Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.
英文关键词	shallow landslide machine learning goodness-of-fit over-fitting GIS Iran
类型	Article
语种	英语
国家	Vietnam ; Iran ; Peoples R China ; Canada ; Malaysia ; South Korea
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000534632500059
WOS关键词	ARTIFICIAL-INTELLIGENCE APPROACH ; SUPPORT VECTOR MACHINE ; FUZZY INFERENCE SYSTEM ; EVIDENTIAL BELIEF FUNCTION ; ERROR PRUNING TREES ; LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; ROTATION FOREST ; RANDOM SUBSPACE ; 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/2XILL650/318556
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 & Labour 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.Univ Kurdistan, Kurdistan Studies Inst, Dept Zrebar Lake Environm Res, Sanandaj 6617715175, Iran; 6.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; 7.Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Shaanxi, Peoples R China; 8.Simon Fraser Univ, Dept Earth Sci, 8888 Univ Dr, Burnaby, BC V5A 1S6, Canada; 9.Minist Forests Lands Nat Resource Operat & Rural, Prince George, BC V2L 1R5, Canada; 10.Res Inst Forests & Rangelands Agr Res Educ & Exte, Tehran 13185116, Iran; 11.Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran 14115111, Iran; 12.Univ Agr Sci & Nat Resources Sari, Fac Nat Resources, Dept Watershed Sci Engn, Mazandaran 4818168984, Iran; 13.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 14.Fac Built Environm & Surveying, Johor Baharu 81310, Malaysia; 15.Korea Inst Geosci & Mineral Resources KIGAM, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; 16.Korea Univ Sci & Technol, Dept Geophys Explorat, 217 Gajeong Ro, Daejeon 34113, South Korea
推荐引用方式 GB/T 7714	Viet-Ha Nhu,Shirzadi, Ataollah,Shahabi, Himan,et al. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran[J],2020,11(4).
APA	Viet-Ha Nhu.,Shirzadi, Ataollah.,Shahabi, Himan.,Chen, Wei.,Clague, John J..,...&Lee, Saro.(2020).Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran.FORESTS,11(4).
MLA	Viet-Ha Nhu,et al."Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran".FORESTS 11.4(2020).