干旱区生态环境知识资源中心(Arid): Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region

Arid

DOI	10.1080/10106049.2021.1892212
	Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region
	Chen, Yunzhi; Chen, Wei; Janizadeh, Saeid; Bhunia, Gouri Sankar; Bera, Amit; Quoc Bao Pham; Nguyen Thi Thuy Linh; Balogun, Abdul-Lateef; Wang, Xiaojing
通讯作者	Linh, NTT (corresponding author), Duy Tan Univ, Inst Res & Dev, Danang, Vietnam. ; Linh, NTT (corresponding author), Duy Tan Univ, Fac Environm & Chem Engn, Danang, Vietnam.
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2021-02
英文摘要	Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model's ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM.
英文关键词	Piping erosion agriculture land use machine learning deep boosting Zarandieh watershed
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000631456600001
WOS关键词	STREAM BANK EROSION ; LOGISTIC-REGRESSION ; SOIL-EROSION ; GULLY-EROSION ; LANDSLIDE SUSCEPTIBILITY ; GOLESTAN PROVINCE ; DECISION TREES ; MACHINE ; WATER ; STATISTICS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352205
作者单位	[Chen, Yunzhi; Chen, Wei; Wang, Xiaojing] Xian Univ Sci & Technol, Coll Geol & Environm, Xian, Shaanxi, Peoples R China; [Chen, Wei] Minist Nat Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian, Peoples R China; [Janizadeh, Saeid] Tarbiat Modares Univ, Fac Nat Resources & Marine Sci, Dept Watershed Management Engn & Sci, Tehran, Iran; [Bhunia, Gouri Sankar] TPF Gentisa Euroestudios SL Gurgaon, Gurgaon, Haryana, India; [Bera, Amit] Indian Inst Engn Sci & Technol, Dept Earth Sci, Sibpur, W Bengal, India; [Quoc Bao Pham] Ton Duc Thang Univ, Environm Qual Atmospher Sci & Climate Change Res, Ho Chi Minh City, Vietnam; [Quoc Bao Pham] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam; [Nguyen Thi Thuy Linh] Duy Tan Univ, Inst Res & Dev, Danang, Vietnam; [Nguyen Thi Thuy Linh] Duy Tan Univ, Fac Environm & Chem Engn, Danang, Vietnam; [Balogun, Abdul-Lateef] Univ Teknol PETRONAS, Dept Civil & Environm Engn, Geospatial Anal & Modelling Res GAMR Lab, Seri Iskandar, Perak, M...
推荐引用方式 GB/T 7714	Chen, Yunzhi,Chen, Wei,Janizadeh, Saeid,et al. Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region[J],2021.
APA	Chen, Yunzhi.,Chen, Wei.,Janizadeh, Saeid.,Bhunia, Gouri Sankar.,Bera, Amit.,...&Wang, Xiaojing.(2021).Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region.GEOCARTO INTERNATIONAL.
MLA	Chen, Yunzhi,et al."Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region".GEOCARTO INTERNATIONAL (2021).