干旱区生态环境知识资源中心(Arid): Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran

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DOI	10.1016/j.apr.2020.08.029
	Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran
	Ebrahimi-Khusfi, Zohre; Taghizadeh-Mehrjardi, Ruhollah; Mirakbari, Maryam
通讯作者	Ebrahimi-Khusfi, Z (corresponding author), Univ Jiroft, Fac Nat Resources, Dept Nat Sci, Jiroft, Iran. ; Taghizadeh-Mehrjardi, R (corresponding author), Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Tubingen, Germany.
来源期刊	ATMOSPHERIC POLLUTION RESEARCH
ISSN	1309-1042
出版年	2021
卷号	12 期号:1 页码:134-147
英文摘要	It is necessary to predict wind erosion events and specify the related effective factors to prioritize management and executive measures to combat desertification caused by wind erosion in arid areas. Therefore, this work aimed to evaluate the applicability of nine machine learning (ML) models (including multivariate adaptive regression splines, least absolute shrinkage and selection operator, k-nearest neighbors, genetic programming, support vector machine, Cubist, artificial neural networks, extreme gradient boosting, random forest) and their average for predicting the seasonal dust storm index (DSI) during 2000-2018 in arid regions of Iran. The results showed that the averaging method outperformed the other individual ML models in predicting DSI changes in all seasons. For instance, the averaging methods improved the prediction accuracies for winter, spring, summer, autumn, and dusty seasons by 22%, 39%, 28%, 32%, and 26%, respectively, compared to the multivariate adaptive regression splines. Furthermore, the most important factors in predicting DSI were detected as follows: wind speed for winter, enhanced vegetation index for spring, maximum wind speed for summer, autumn and dusty seasons. In general, our results indicate that the combining of the individual ML models by averaging method help us to develop a more accurate approach for predicting the temporal changes of the dust events in arid regions. Furthermore, the obtained results in this study can be applicable for prioritizing measures in order to minimize the dangers of wind erosion based on the major driving factors.
英文关键词	Machine learning Remote sensing data Climatic parameters Dust emissions Dry lands Iran
类型	Article
语种	英语
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000618659300001
WOS关键词	SUPPORT VECTOR REGRESSION ; NEAREST-NEIGHBOR APPROACH ; WIND EROSION ; RANDOM-FOREST ; PM2.5 CONCENTRATIONS ; NEURAL-NETWORKS ; SOIL-MOISTURE ; AVERAGING METHODS ; SISTAN REGION ; VEGETATION
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/2XILL650/349647
作者单位	[Ebrahimi-Khusfi, Zohre] Univ Jiroft, Fac Nat Resources, Dept Nat Sci, Jiroft, Iran; [Taghizadeh-Mehrjardi, Ruhollah] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah] Ardakan Univ, Fac Agr & Nat Resources, Ardakan, Iran; [Mirakbari, Maryam] Univ Tehran, Fac Nat Resources, Tehran, Iran
推荐引用方式 GB/T 7714	Ebrahimi-Khusfi, Zohre,Taghizadeh-Mehrjardi, Ruhollah,Mirakbari, Maryam. Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran[J],2021,12(1):134-147.
APA	Ebrahimi-Khusfi, Zohre,Taghizadeh-Mehrjardi, Ruhollah,&Mirakbari, Maryam.(2021).Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran.ATMOSPHERIC POLLUTION RESEARCH,12(1),134-147.
MLA	Ebrahimi-Khusfi, Zohre,et al."Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran".ATMOSPHERIC POLLUTION RESEARCH 12.1(2021):134-147.