干旱区生态环境知识资源中心(Arid): GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran

Arid

DOI	10.3390/rs12152478
	GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran
	Lei, Xinxiang; Chen, Wei; Avand, Mohammadtaghi; Janizadeh, Saeid; Kariminejad, Narges; Shahabi, Hejar; Costache, Romulus; Shahabi, Himan; Shirzadi, Ataollah; Mosavi, Amir
通讯作者	Mosavi, A
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:15
英文摘要	In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.
英文关键词	machine learning GIS gully erosion susceptibility mapping head-cut erosion Iran
类型	Article
语种	英语
开放获取类型	Other Gold
收录类别	SCI-E
WOS记录号	WOS:000559177400001
WOS关键词	KERNEL LOGISTIC-REGRESSION ; FUZZY INFERENCE SYSTEM ; DATA-MINING TECHNIQUES ; REMOTE-SENSING DATA ; NAIVE BAYES TREE ; FREQUENCY RATIO ; SPATIAL PREDICTION ; RANDOM FORESTS ; DECISION TREE ; METAHEURISTIC OPTIMIZATION
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325596
作者单位	[Lei, Xinxiang; Chen, Wei] Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; [Chen, Wei] Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; [Avand, Mohammadtaghi; Janizadeh, Saeid] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran 14115111, Iran; [Kariminejad, Narges] Gorgan Univ Agr Sci & Nat Resources, Dept Watershed & Arid Zone Management, Gorgan 49189434, Golestan, Iran; [Shahabi, Hejar] Univ Tabriz, Dept Remote Sensing, Tabriz 5166616471, Iran; [Shahabi, Hejar] Univ Tabriz, GIS, Tabriz 5166616471, Iran; [Costache, Romulus] Univ Bucharest, Res Inst, 90-92 Sos Panduri,5th Dist, Bucharest 013686, Romania; [Costache, Romulus] Natl Inst Hydrol & Water Management, Bucuresti Ploiesti Rd 97E,1st Dist, Bucharest 013686, Romania; [Shahabi, Himan] Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj 6617715175, Iran; [Shahabi, Himan] Univ Kurdistan, Kurdistan Studies Inst, Dept...
推荐引用方式 GB/T 7714	Lei, Xinxiang,Chen, Wei,Avand, Mohammadtaghi,et al. GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran[J],2020,12(15).
APA	Lei, Xinxiang.,Chen, Wei.,Avand, Mohammadtaghi.,Janizadeh, Saeid.,Kariminejad, Narges.,...&Mosavi, Amir.(2020).GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran.REMOTE SENSING,12(15).
MLA	Lei, Xinxiang,et al."GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran".REMOTE SENSING 12.15(2020).