干旱区生态环境知识资源中心(Arid): Hybrid regularization and weighted subspace algorithms with random forest model for assessing piping erosion in semi-arid ecosystem

Arid

DOI	10.1007/s12665-023-11190-w
	Hybrid regularization and weighted subspace algorithms with random forest model for assessing piping erosion in semi-arid ecosystem
	Lu, Quang-Oai; Ahmadi, Kourosh; Mahmoodi, Shirin; Karami, Ayoob; Elkhrachy, Ismail; Mondal, Ismail; Arshad, Arfan; Nguyen, Trinh Trong; Chi, Nguyen Thuy Lan; Thai, Van Nam
通讯作者	Thai, VN
来源期刊	ENVIRONMENTAL EARTH SCIENCES
ISSN	1866-6280
EISSN	1866-6299
出版年	2023
卷号	82 期号:22
英文摘要	Land degradation encompasses ecological, biological, and physical deterioration of land resulting from natural or anthropogenic factors, including soil erosion. Piping erosion, specifically in arid and semi-arid regions, is recognized as a significant cause of land degradation. Therefore, it is crucial to identify the influential factors and spatially model this phenomenon to effectively control it. This investigation aims to study the spatial modeling and evaluation of piping erosion using machine learning models: random forest (RF) and two hybrid models, regularized random forest (RRF) and weighted subspace random forest (WSRF), in the Khoshkrood watershed of Markazi Province, Iran. In this study, 70% of the 179 recorded locations of piping erosion were randomly selected for model construction, while the remaining 30% were used for model validation. Fourteen geo-environmental variables affecting piping erosion were used as independent factors for modeling. The performance of the machine learning models was evaluated using five criteria, including sensitivity, specificity, and area under the curve (AUC). The results demonstrated that all three models performed well in predicting piping erosion. The AUC values for the WSRF, RRF, and RF models were 80.93%, 80.43%, and 79.11%, respectively. Moreover, the analysis of relative variable importance revealed that elevation was the most influential variable across all three models. The findings emphasize the significance of utilizing machine learning models for predicting and managing land degradation phenomena. By identifying influential geo-environmental and employing accurate spatial models, effective control measures can be implemented to mitigate the adverse impacts of piping erosion in arid and semi-arid regions.
英文关键词	Land degradation Piping erosion Hybrid machine learning Regularized random forest (RRF) Weighted subspace random forest (WSRF)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001095702700002
WOS关键词	LAND DEGRADATION ; SOIL-EROSION ; SUSCEPTIBILITY ; GIS ; CLASSIFICATION ; SELECTION ; MACHINE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396134
推荐引用方式 GB/T 7714	Lu, Quang-Oai,Ahmadi, Kourosh,Mahmoodi, Shirin,et al. Hybrid regularization and weighted subspace algorithms with random forest model for assessing piping erosion in semi-arid ecosystem[J],2023,82(22).
APA	Lu, Quang-Oai.,Ahmadi, Kourosh.,Mahmoodi, Shirin.,Karami, Ayoob.,Elkhrachy, Ismail.,...&Thai, Van Nam.(2023).Hybrid regularization and weighted subspace algorithms with random forest model for assessing piping erosion in semi-arid ecosystem.ENVIRONMENTAL EARTH SCIENCES,82(22).
MLA	Lu, Quang-Oai,et al."Hybrid regularization and weighted subspace algorithms with random forest model for assessing piping erosion in semi-arid ecosystem".ENVIRONMENTAL EARTH SCIENCES 82.22(2023).