干旱区生态环境知识资源中心(Arid): GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

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DOI	10.3390/app10062039
	GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models
	Viet-Ha Nhu 1,2; Janizadeh, Saeid 3; Avand, Mohammadtaghi 3; Chen, Wei 4; Farzin, Mohsen 5; Omidvar, Ebrahim 6; Shirzadi, Ataollah 7; Shahabi, Himan 8,9; Clague, John J.10; Jaafari, Abolfazl 11; Mansoorypoor, Fatemeh 12; 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	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2020
卷号	10 期号:6
英文摘要	Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision-recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.
英文关键词	gully erosion watershed management machine learning hybrid models 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:000529252800140
WOS关键词	ARTIFICIAL-INTELLIGENCE APPROACH ; BIOGEOGRAPHY-BASED OPTIMIZATION ; MACHINE LEARNING-MODELS ; SUPPORT VECTOR MACHINE ; FUZZY INFERENCE SYSTEM ; MAXIMUM-ENTROPY MODEL ; LANDSLIDE SUSCEPTIBILITY ; SPATIAL PREDICTION ; LOGISTIC-REGRESSION ; DECISION TREE
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/318537
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.Tarbiat Modares Univ, Coll Nat Resources, Dept Watershed Management Engn, POB 14115-111, Tehran, Iran; 4.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; 5.Univ Yasuj, Fac Agr & Nat Resources, Dept Forestry Range & Watershed Management, Yasuj 7591874934, Iran; 6.Univ Kashan, Fac Nat Resources & Earth Sci, Dept Rangeland & Watershed Management, Kashan 8731753153, Iran; 7.Univ Kurdistan, Fac Nat Resources, Dept Rangeland & Watershed Management, Sanandaj 6617715175, Iran; 8.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj 6617715175, Iran; 9.Univ Kurdistan, Kurdistan Studies Inst, Dept Zrebar Lake Environm Res, Sanandaj 6617715175, Iran; 10.Simon Fraser Univ, Dept Earth Sci, Burnaby, BC V5A 1S6, Canada; 11.AREEO, Res Inst Forests & Rangelands, POB 64414-356, Tehran, Iran; 12.Univ Tehran, Coll Farabi, Dept Engn, Data Min Lab, Tehran 3718117469, Iran; 13.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 14.Univ Teknol Malaysia, Fac Built Environm & Surveying, Dept Geoinformat, 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,Janizadeh, Saeid,Avand, Mohammadtaghi,et al. GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models[J],2020,10(6).
APA	Viet-Ha Nhu.,Janizadeh, Saeid.,Avand, Mohammadtaghi.,Chen, Wei.,Farzin, Mohsen.,...&Lee, Saro.(2020).GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models.APPLIED SCIENCES-BASEL,10(6).
MLA	Viet-Ha Nhu,et al."GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models".APPLIED SCIENCES-BASEL 10.6(2020).