干旱区生态环境知识资源中心(Arid): Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment

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DOI	10.1016/j.jclepro.2022.132428
	Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment
	Bag, Rakhohori; Mondal, Ismail; Dehbozorgi, Mahroo; Bank, Subhra Pratim; Das, Dipendra Nath; Bandyopadhyay, Jatisankar; Pham, Quoc Bao; Al-Quraishi, Ayad M. Fadhil; Nguyen, Xuan Cuong
通讯作者	Nguyen, XC
来源期刊	JOURNAL OF CLEANER PRODUCTION
ISSN	0959-6526
EISSN	1879-1786
出版年	2022
卷号	364
英文摘要	Sobha watershed, located in the Puruliya district of West Bengal, India, is experiencing severe soil erosion due to specific geo-environmental settings and unscientific land practices. It poses serious threats to agricultural and natural resource development, resulting in land degradation and desertification. This study attempts to identify soil erosion susceptible zones (SESZ) of the Sobha watershed by utilising remote sensing and GIS data products in different machine learning algorithms i.e., Support Vector Machine (SVM), Classification and Regression Tree (CART), Boosted Regression Tree (BRT), and Random Forest (RF)) considering sixteen soil erosion controlling factors (SECFs). In addition, the efficiency of the chosen machine learning models was evaluated using known soil erosion and non-erosion data. The results showed that elevation, drainage density (DD), and normalised difference vegetation index (NDVI) factors contribute the most to soil erosion. The ROC (receiver operating curve) AUC (area under the curve) is used to compare each model, and it was reveals that the RF model performed and predicted the best among them. However, all the models exhibit an outstanding capacity with AUC > 85% (RF = 0.97, BRT = 0.96, SVM = 0.95, and CART = 0.88). The RF model results show that the Northeastern portion of the catchment (upper part) is most vulnerable to erosion, and about 14.48% of the basin areas are under the severe erosion zone. Thereby, the findings based on machine learning algorithms and intensive field visits are utilised to assess the soil erosion risk zones, and this work will give insight into implementing suitable policies to mitigate this issue. Furthermore, the approaches utilised in this study could be useful in predicting soil erosion risk in other regions as well. In addition, the study has also recommended some appropriate policies and management approaches that would be immensely useful for the local government and policymakers in initiating strategic planning to combat soil erosion.
英文关键词	Soil erosion Soil erosion susceptibility Spatial prediction Machine learning Land degradation Remote sensing & GIS Ajodhya Hill
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000821902600001
WOS关键词	DECISION-MAKING ; REGRESSION ; CLASSIFICATION ; GRADIENT ; PRIORITIZATION ; RESERVOIR ; TERRAIN ; SYSTEMS ; BASIN ; CART
WOS类目	Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences
WOS研究方向	Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393369
推荐引用方式 GB/T 7714	Bag, Rakhohori,Mondal, Ismail,Dehbozorgi, Mahroo,et al. Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment[J],2022,364.
APA	Bag, Rakhohori.,Mondal, Ismail.,Dehbozorgi, Mahroo.,Bank, Subhra Pratim.,Das, Dipendra Nath.,...&Nguyen, Xuan Cuong.(2022).Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment.JOURNAL OF CLEANER PRODUCTION,364.
MLA	Bag, Rakhohori,et al."Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment".JOURNAL OF CLEANER PRODUCTION 364(2022).