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DOI	10.1007/s11356-020-10957-z
	Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions
	Ebrahimi-Khusfi, Zohre; Taghizadeh-Mehrjardi, Ruhollah; Nafarzadegan, Ali Reza
通讯作者	Ebrahimi-Khusfi, Z
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
英文摘要	Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods. Furthermore, we used a model-agnostic interpretation to make the ANFIS model interpretable. In addition, we used the bat optimization algorithm (BAT) to increase the prediction accuracy of the ANFIS model. Seven explanatory variables were chosen for predicting DC in the cold and warm months across semi-arid regions of Iran. The results showed that the ANFIS+BAT model increased the correlation coefficient by 10% and 16% for predicting DC in the cold and warm months, respectively, compared with the ANFIS model. Furthermore, the uncertainty analysis indicated a lower prediction interval (i.e., lower uncertainty) for the ANFIS+BAT model compared with the ANFIS model for predicting DC in the cold and warm months. In addition, the model-agnostic interpretation tool findings indicated the highest contributions of air temperature and maximum wind speed for predicting DC in the cold and warm months, respectively. Prediction of DC using the proposed model will allow decision-makers to better plan for measures to mitigate the risks of wind erosion and air pollution.
英文关键词	Air pollution ANFIS Bat optimization algorithm Interpretability Machine learning Uncertainty
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000574802300004
WOS关键词	FUZZY INFERENCE SYSTEM ; WIND EROSION ; AIR-QUALITY ; NORTHERN CHINA ; SOIL-MOISTURE ; MLR MODELS ; EEMD-GRNN ; VEGETATION ; STORMS ; SPEED
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/2XILL650/328309
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; [Nafarzadegan, Ali Reza] Univ Hormozgan, Dept Nat Resources Engn, Bandar Abbas, Hormozgan, Iran
推荐引用方式 GB/T 7714	Ebrahimi-Khusfi, Zohre,Taghizadeh-Mehrjardi, Ruhollah,Nafarzadegan, Ali Reza. Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions[J].
APA	Ebrahimi-Khusfi, Zohre,Taghizadeh-Mehrjardi, Ruhollah,&Nafarzadegan, Ali Reza.
MLA	Ebrahimi-Khusfi, Zohre,et al."Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH