干旱区生态环境知识资源中心(Arid): Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory

Arid

DOI	10.1016/j.ecolind.2021.108287
	Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory
	Ebrahimi-Khusfi, Zohre; Taghizadeh-Mehrjardi, Ruhollah; Roustaei, Fatemeh; Ebrahimi-Khusfi, Mohsen; Mosavi, Amir Hosein; Heung, Brandon; Soleimani-Sardo, Mojtaba; Scholten, Thomas
通讯作者	Ebrahimi-Khusfi, Z (corresponding author), Univ Jiroft, Fac Nat Resources, Dept Ecol Engn, Jiroft, Iran. ; Mosavi, AH (corresponding author), Obuda Univ, Inst Software Design & Dev, H-1034 Budapest, Hungary.
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2021
卷号	132
英文摘要	Dust pollution is one of the major environmental crises in the arid regions of Iran and there is a need to predict dust pollution and identify its controlling factors to help reduce its adverse effects on the livelihood of residents of these areas. Although deep neural networks (DNN) are powerful tools in the modelling of environmental phenomena, they are recognized as being challenging to interpret due to their black-box nature. To address this issue and understand the importance of each environmental control on dust pollution, game theory (i.e., Shapley values) was used to better understand the performance and interpretability of DNN models. Here, monthly mean values of precipitation, air temperature, surface wind speed, potential evapotranspiration, normalized difference vegetation index, normalized difference salinity index, Palmer drought severity index, soil heat flux, and surface pressure were selected as explanatory variables. The dust storm index (DSI), an indicator of dust pollution, was the predicted response variable for the cold and warm months. The results showed that the accuracies of the DNN model in predicting cold months DSI (CMDSI) and warm months DSI (WMDSI) were higher compared to other traditional machine learning algorithms. DNN model increased the R2 by 13% and 15% for predicting CMDSI and WMDSI, respectively, compared to the Random Forest model, which was the second most effective approach. According to the Shapley values, the most important controls on the occurrence of dust storms during the cold months of the study period (2000-2018) were wind speed, soil heat flux, and precipitation. During the warm months, wind speed was the most important controlling factor and was followed by precipitation, soil heat flux, and potential evapotranspiration. Overall, the results demonstrate the effectiveness of the DNN model and game theory in identifying the factors affecting dust pollution, which may help mitigate its impacts on the residents of western Iran.
英文关键词	Air quality Deep neural network Dust events Middle East Remote sensing Shapley values
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000710987500001
WOS关键词	SUPPORT VECTOR MACHINE ; MEAN ABSOLUTE ERROR ; WIND EROSION ; TEMPORAL VARIATIONS ; GAUSSIAN-PROCESSES ; AIR-TEMPERATURE ; SOIL-MOISTURE ; PREDICTION ; EMISSIONS ; DROUGHT
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/2XILL650/368148
作者单位	[Ebrahimi-Khusfi, Zohre] Univ Jiroft, Fac Nat Resources, Dept Ecol Engn, Jiroft, Iran; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, CRC 1070 ResourceCultures, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Roustaei, Fatemeh] Ardakan Univ, Fac Agr & Nat Resources, Dept Nat Engn, Ardakan, Iran; [Ebrahimi-Khusfi, Mohsen] Yazd Univ, Fac Humanities & Social Sci, Dept Geog, Yazd, Iran; [Mosavi, Amir Hosein] Obuda Univ, Inst Software Design & Dev, H-1034 Budapest, Hungary; [Heung, Brandon] Dalhousie Univ, Fac Agr, Dept Plant Food & Environm Sci, Halifax, NS, Canada; [Soleimani-Sardo, Mojtaba] Univ Jiroft, Fac Nat Resources, Dept Environm Sci & Engn, Jiroft, Iran; [Scholten, Thomas] Univ Tubingen, DFG Cluster Excellence Machine Learning, Tubingen, Germany
推荐引用方式 GB/T 7714	Ebrahimi-Khusfi, Zohre,Taghizadeh-Mehrjardi, Ruhollah,Roustaei, Fatemeh,et al. Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory[J],2021,132.
APA	Ebrahimi-Khusfi, Zohre.,Taghizadeh-Mehrjardi, Ruhollah.,Roustaei, Fatemeh.,Ebrahimi-Khusfi, Mohsen.,Mosavi, Amir Hosein.,...&Scholten, Thomas.(2021).Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory.ECOLOGICAL INDICATORS,132.
MLA	Ebrahimi-Khusfi, Zohre,et al."Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory".ECOLOGICAL INDICATORS 132(2021).