干旱区生态环境知识资源中心(Arid): Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)

Arid

DOI	10.1155/2021/6610228
	Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)
	Aghelpour, Pouya; Varshavian, Vahid
通讯作者	Varshavian, V (corresponding author), Bu Ali Sina Univ, Fac Agr, Dept Water Engn, Agr Meteorol, Hamadan, Hamadan, Iran.
来源期刊	COMPLEXITY
ISSN	1076-2787
EISSN	1099-0526
出版年	2021
卷号	2021
英文摘要	Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index (MSPI) can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm (MLP-ICA) have been investigated for the first time in multivariate drought studies. For this purpose, two semi-arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization (IRIMO), during the period of 1988-2017. MSPI was calculated in 5-time windows of the multivariate drought, including MSPI3-6 (drought in perspectives of soil moisture and surface hydrology simultaneously), MSPI6-12 (hydrological and agricultural droughts simultaneously), MSPI3-12 (soil moisture, surface hydrology, and agricultural droughts simultaneously), MSPI12-24 (drought in perspectives of agriculture and groundwater simultaneously), and MSPI24-48 (socio-economical droughts). The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows (MSPI12-24 and MSPI24-48) had better predictions than the smaller ones (MSPI3-6, MSPI6-12, and MSPI3-12). Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously (MSPI3-6). The hybrid MLP-ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP's accuracy by 28.5%, which is a significant improvement. According to the evaluations (RMSE = 0.20; MAE = 0.15; R = 0.95), the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas.
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:000616107800003
WOS类目	Mathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS研究方向	Mathematics ; Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369048
作者单位	[Aghelpour, Pouya; Varshavian, Vahid] Bu Ali Sina Univ, Fac Agr, Dept Water Engn, Agr Meteorol, Hamadan, Hamadan, Iran
推荐引用方式 GB/T 7714	Aghelpour, Pouya,Varshavian, Vahid. Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)[J],2021,2021.
APA	Aghelpour, Pouya,&Varshavian, Vahid.(2021).Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA).COMPLEXITY,2021.
MLA	Aghelpour, Pouya,et al."Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)".COMPLEXITY 2021(2021).