干旱区生态环境知识资源中心(Arid): Predicting wind erosion rate using portable wind tunnel combined with machine learning algorithms in calcareous soils, southern Iran

Arid

DOI	10.1016/j.jenvman.2021.114171
	Predicting wind erosion rate using portable wind tunnel combined with machine learning algorithms in calcareous soils, southern Iran
	Mina, Monireh; Rezaei, Mahrooz; Sameni, Abdolmajid; Ostovari, Yaser; Ritsema, Coen
通讯作者	Rezaei, M (corresponding author)，Wageningen Univ & Res, Meteorol & Air Qual Grp, POB 47, NL-6700 AA Wageningen, Netherlands.
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2022
卷号	304
英文摘要	Wind erosion is a critical factor in land degradation worldwide, particularly in arid and semi-arid regions of southern Iran, which have been severely exposed to wind erosion in the recent years due to climate change and land use changes. The main objective of the present study was to predict the wind erosion rate (WER) using easily measurable soil properties combined with some data mining approaches. For this purpose, the WER was measured at 100 locations with different land uses and soil types in the Fars Province, southern Iran using a portable wind tunnel. The WER was predicted by multiple linear regression (MLR), support vector regression (SVR) and decision tree (DT) algorithms using easily measurable soil properties. Results revealed that land use and soil type had significant effect on the WER. The highest mean WER was observed in Entisols with the lowest organic matter (OM), the lowest penetration resistance (PR) and the lowest aggregate mean weight diameter (MWD). Bare lands with the lowest OM and MWD showed the highest WER compared to other land uses. R-2 and RMSE of the non-linear regression models developed based on the type of the relationship between the WER and easily measurable soil properties improved by 15% and 12%, respectively, compared to the linear regression model. In both train and test datasets, the SVR and DT models coupled to a genetic algorithm (GA) used for selecting the effective easily measurable soil properties had higher performance than the SVR and DT models using all easily measurable soil properties for predicting WER. With respect to statistical indices, the SVR model with R-2 = 0.91 and RMSE = 0.68 g m(-2) s(-1) outperformed the MLR and DT for predicting the WER. We concluded that combining the SVR with GA could be an applicable and promising method for predicting WER.
英文关键词	Decision tree Dust emission Genetic algorithm Land degradation
类型	Article
语种	英语
开放获取类型	hybrid, Green Published
收录类别	SCI-E
WOS记录号	WOS:000741771500003
WOS关键词	SUPPORT VECTOR MACHINES ; THRESHOLD FRICTION VELOCITY ; USLE K-FACTOR ; PEDOTRANSFER FUNCTIONS ; ERODIBLE FRACTION ; WATER-RETENTION ; ERODIBILITY ; MOISTURE ; SAND ; ENTRAINMENT
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376892
作者单位	[Mina, Monireh; Rezaei, Mahrooz; Sameni, Abdolmajid] Shiraz Univ, Sch Agr, Dept Soil Sci, Shiraz, Iran; [Rezaei, Mahrooz] Wageningen Univ & Res, Meteorol & Air Qual Grp, POB 47, NL-6700 AA Wageningen, Netherlands; [Ostovari, Yaser] Tech Univ Munich, TUM Sch Life Sci Weihenstephan, Res Dept Ecol & Ecosyst Management, Chair Soil Sci, Freising Weihenstephan, Germany; [Ritsema, Coen] Wageningen Univ & Res, Soil Phys & Land Management Grp, NL-6700 AA Wageningen, Netherlands
推荐引用方式 GB/T 7714	Mina, Monireh,Rezaei, Mahrooz,Sameni, Abdolmajid,et al. Predicting wind erosion rate using portable wind tunnel combined with machine learning algorithms in calcareous soils, southern Iran[J],2022,304.
APA	Mina, Monireh,Rezaei, Mahrooz,Sameni, Abdolmajid,Ostovari, Yaser,&Ritsema, Coen.(2022).Predicting wind erosion rate using portable wind tunnel combined with machine learning algorithms in calcareous soils, southern Iran.JOURNAL OF ENVIRONMENTAL MANAGEMENT,304.
MLA	Mina, Monireh,et al."Predicting wind erosion rate using portable wind tunnel combined with machine learning algorithms in calcareous soils, southern Iran".JOURNAL OF ENVIRONMENTAL MANAGEMENT 304(2022).