干旱区生态环境知识资源中心(Arid): Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Arid

DOI	10.3390/ijerph17082749
	Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
	Viet-Ha Nhu 1,2; Shirzadi, Ataollah 3; Shahabi, Himan 4,5; Singh, Sushant K.6; Al-Ansari, Nadhir 7; Clague, John J.8; Jaafari, Abolfazl 9; Chen, Wei 10,11; Miraki, Shaghayegh 12; Dou, Jie 13; Luu, Chinh 14; Gorski, Krzysztof 15; Binh Thai Pham 16; Huu Duy Nguyen 17; Bin Ahmad, Baharin 18
通讯作者	Al-Ansari, Nadhir ; Binh Thai Pham
来源期刊	INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
EISSN	1660-4601
出版年	2020
卷号	17 期号:8
英文摘要	Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naive Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
英文关键词	Shallow landslide artificial intelligence prediction accuracy logistic model tree goodness-of-fit Iran
类型	Article
语种	英语
国家	Vietnam ; Iran ; USA ; Sweden ; Canada ; Peoples R China ; Japan ; Poland ; Malaysia
开放获取类型	gold, Green Published
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000535744100134
WOS关键词	BIOGEOGRAPHY-BASED OPTIMIZATION ; FUZZY INFERENCE SYSTEM ; ANALYTICAL HIERARCHY PROCESS ; EVIDENTIAL BELIEF FUNCTION ; ERROR PRUNING TREES ; SPATIAL PREDICTION ; RANDOM FOREST ; FREQUENCY RATIO ; DECISION TREE ; INTELLIGENCE APPROACH
WOS类目	Environmental Sciences ; Public, Environmental & Occupational Health
WOS研究方向	Environmental Sciences & Ecology ; Public, Environmental & Occupational Health
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/318569
作者单位	1.Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City 758307, Vietnam; 2.Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City 758307, 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.Virtusa Corp, 10 Marshall St, Irvington, NJ 07111 USA; 7.Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden; 8.Simon Fraser Univ, Dept Earth Sci, Burnaby, BC V5A 1S6, Canada; 9.Agr Res Educ & Extens Org AREEO, Res Inst Forests & Rangelands, Tehran 13185116, Iran; 10.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; 11.Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; 12.Univ Agr Sci & Nat Resources Sari, Fac Nat Resources, Dept Watershed Sci Engn, Mazandaran 4818168984, Iran; 13.Nagaoka Univ Technol, Dept Civil & Environm Engn, 1603-1 Kami Tomioka, Nagaoka, Niigata 9402188, Japan; 14.Natl Univ Civil Engn, Fac Hydraul Engn, Hanoi 112000, Vietnam; 15.Kazimierz Pulaski Univ Technol & Humanities Radom, Fac Mech Engn, Chrobrego 45 St, PL-26200 Radom, Poland; 16.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 17.VNU Univ Sci, Fac Geog, 334 Nguyen Trai, Hanoi 100000, Vietnam; 18.Univ Teknol Malaysia UTM, Fac Built Environm & Surveying, Johor Baharu 81310, Malaysia
推荐引用方式 GB/T 7714	Viet-Ha Nhu,Shirzadi, Ataollah,Shahabi, Himan,et al. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms[J],2020,17(8).
APA	Viet-Ha Nhu.,Shirzadi, Ataollah.,Shahabi, Himan.,Singh, Sushant K..,Al-Ansari, Nadhir.,...&Bin Ahmad, Baharin.(2020).Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH,17(8).
MLA	Viet-Ha Nhu,et al."Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms".INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 17.8(2020).