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DOI	10.3390/s19112444
	A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)
	Dieu Tien Bui 1,2; Shirzadi, Ataollah 3; Shahabi, Himan 4; Chapi, Kamran 3; Omidavr, Ebrahim 5; Binh Thai Pham 6; Asl, Dawood Talebpour 4; Khaledian, Hossein 7; Pradhan, Biswajeet 8,9; Panahi, Mahdi 10; Bin Ahmad, Baharin 11; Rahmani, Hosein 12; Grof, Gyula 13; Lee, Saro 14,15
通讯作者	Shirzadi, Ataollah ; Shahabi, Himan ; Lee, Saro
来源期刊	SENSORS
EISSN	1424-8220
出版年	2019
卷号	19 期号:11
英文摘要	In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naive Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
英文关键词	gully erosion machine learning ensemble algorithms geomorphology Geographic information science Kurdistan province
类型	Article
语种	英语
国家	Vietnam ; Iran ; Australia ; South Korea ; Malaysia ; Hungary
开放获取类型	Green Published, gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000472133300022
WOS关键词	DATA-MINING TECHNIQUES ; FLOOD SUSCEPTIBILITY ASSESSMENT ; FUZZY INFERENCE SYSTEM ; NAIVE BAYES TREE ; LOGISTIC-REGRESSION ; ROTATION FOREST ; SPATIAL PREDICTION ; OPTIMIZATION ALGORITHMS ; DISCRIMINANT-ANALYSIS ; CLASSIFIER ENSEMBLE
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218843
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 Kashan, Fac Nat Resources & Earth Sci, Dept Rangeland & Watershed Management, Kashan 8731753153, Iran; 6.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 7.AREEO, Kurdistan Agr & Nat Resources Res & Educ Ctr, Sanandaj 6616936311, Iran; 8.Univ Technol Sydney, Fac Engn & IT, Ctr Adv Modeling & Geospatial Syst CAMGIS, CB11-06-106,Bldg 11,81 Broadway, Ultimo, NSW 2007, Australia; 9.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; 10.Islamic Azad Univ, North Tehran Branch, Young Researchers & Elites Club, Dept Geophys, POB 19585-466, Tehran, Iran; 11.UTM, Fac Built Environm & Surveying, Johor Baharu 81310, Malaysia; 12.Shiraz Univ, Sch Elect & Comp Engn, Dept Comp Sci & Engn & IT, Shiraz 8433471964, Iran; 13.Budapest Univ Technol & Econ, Dept Energy Engn, Budapest 1111, Hungary; 14.Korea Inst Geosci & Mineral Resources KIGAM, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; 15.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. A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)[J],2019,19(11).
APA	Dieu Tien Bui.,Shirzadi, Ataollah.,Shahabi, Himan.,Chapi, Kamran.,Omidavr, Ebrahim.,...&Lee, Saro.(2019).A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran).SENSORS,19(11).
MLA	Dieu Tien Bui,et al."A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)".SENSORS 19.11(2019).