干旱区生态环境知识资源中心(Arid): Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques

Arid

DOI	10.1016/j.ecolind.2021.107499
	Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques
	Ebrahimi-Khusfi, Zohre; Nafarzadegan, Ali Reza; Dargahian, Fatemeh
通讯作者	Ebrahimi-Khusfi, Z (corresponding author), Univ Jiroft, Fac Nat Resources, Dept Ecol Engn, Jiroft, Iran. ; Nafarzadegan, AR (corresponding author), Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran.
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2021
卷号	125
英文摘要	In the past decades, some desert wetlands have become critical regions for dust production in the arid and semiarid regions of the world. Accurate prediction of the number of dusty days (NDDs) in these areas is of great importance. The most popular method for predicting climatic and environmental variables is machine learning (ML). Although it has received more attention for spatial prediction, it has received less attention for the temporal prediction of these variables. This work is the first effort to predict NDDs in the major source of dust production in southeastern Iran using ML models and different feature selection (FS) techniques. For this purpose, monthly data of 21 predictor variables related to the study period (1988?2017) was used to predict the target variable (NDDs). The main aim was to evaluate the support vector machine (SVM), conditional inference random forest (CRF), and stochastic gradient boosting (SGB) models based on three FS algorithms, including Boruta, multivariate adaptive regression splines (MARS), and recursive feature elimination (RFE) techniques in predicting NDDs around the Hamoun wetlands. After analyzing the collinearity effect and removing the independent variables with a Tolerance < 0.11, the best attributes were selected to train the SVM, SGB, and CRF models. All datasets were randomly classified into training (70%) and verification (30%) sets. The performance of models was evaluated based on the determination coefficient (R-square), root mean square error (RMSE), mean absolute error (MAE), and Nash Sutcliffe efficiency (NSE) coefficient related to holdout data. The results indicated that SGB-MARS, SGB-RFE, and SGB-Boruta outperformed other models with different FS techniques, in terms of R2 (0.9), RMSE (2.5), MAE (1.9), and NSE (0.9). Furthermore, surface winds speed, maximum air temperature, relative humidity, wetland dried bed, and erosive winds frequency were detected as the most important factors for predicting NDDs in the study area. This study encourages us to use the SGB model with various FS techniques to predict NDDs around the desert wetlands. These results can help decision-makers reduce the risks of dust emission and increase the safety of residents around the desert wetlands.
英文关键词	Dust emissions Boruta MARS Recursive feature elimination Multicollinearity Stochastic gradient boosting Hamoun wetlands
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000637200600003
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350003
作者单位	[Ebrahimi-Khusfi, Zohre] Univ Jiroft, Fac Nat Resources, Dept Ecol Engn, Jiroft, Iran; [Nafarzadegan, Ali Reza] Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran; [Dargahian, Fatemeh] Agr Res Educ & Extens Org AREEO, Res Inst Forests & Rangelands, Desert Res Div, Tehran, Iran
推荐引用方式 GB/T 7714	Ebrahimi-Khusfi, Zohre,Nafarzadegan, Ali Reza,Dargahian, Fatemeh. Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques[J],2021,125.
APA	Ebrahimi-Khusfi, Zohre,Nafarzadegan, Ali Reza,&Dargahian, Fatemeh.(2021).Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques.ECOLOGICAL INDICATORS,125.
MLA	Ebrahimi-Khusfi, Zohre,et al."Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques".ECOLOGICAL INDICATORS 125(2021).