干旱区生态环境知识资源中心(Arid): Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area

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DOI	10.1007/s10668-021-01784-4
	Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area
	Farzandi, Mahboobeh; Sanaeinejad, Hossein; Rezaei-Pazhan, Hojat; Sarmad, Majid
通讯作者	Farzandi, M (corresponding author), Ferdowsi Univ Mashhad, Fac Agr, Dept Water Engn, Mashhad, Razavi Khorasan, Iran.
来源期刊	ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
ISSN	1387-585X
EISSN	1573-2975
出版年	2021-09
英文摘要	Precipitation is among the main variables in weather and climate studies. The length of the statistical period plays a pivotal role in the accurate analysis of precipitation. One of the limitations of meteorological stations is having missing data. Analysis based on incomplete data leads to biased analysis. The historical monthly precipitation of the five stations in Iran is available since 1880 with missing data. The name of these synoptic stations are Mashhad, Isfahan, Tehran, Bushehr, and Jask. The data in the period of 1941-1949 have a gap that was during and following World War II (1939-1945). The present study aimed to use several classic and meta-heuristic methods to estimate these missing data. The Root Means Square Error (RMSE) criteria were used for comparison. The neighboring stations of Iran were selected as independent variable to estimate missing rainfall data. First, missing data were restored with the fitting of several new regression models for monthly precipitation (with RMSEs: 9.79, 7.89, 13.43, 6.65, and 20.96 millimeter(mm)). Then, the parameters of regression models were optimized by methods of genetic algorithm (GA) and Ant Colony (ACO). It was observed that RMSEs reduced to 2.56, 2.51, 3.49, 2.48, and 4.02 mm. Besides, Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were used to model the data. ANN and SVR could not increase the accuracy of the estimated data. The missing data were imputed using evolutionary methods (GA and ACO). As a result, the length of the statistical period of the stations reached over 125 years, and the data could be considered a valuable basis for water resources, drought analyses, evaluation trends, climate changes, and global warming.
英文关键词	Data assimilation Precipitation of Iran Missing data Genetic algorithm Ant colony
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000698125600001
WOS关键词	IMPUTATION ; CLIMATE
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363092
作者单位	[Farzandi, Mahboobeh; Sanaeinejad, Hossein] Ferdowsi Univ Mashhad, Fac Agr, Dept Water Engn, Mashhad, Razavi Khorasan, Iran; [Rezaei-Pazhan, Hojat] Islamic Azad Univ, Fac Engn, Dept Civil Engn, Mashhad Branch, Mashhad, Razavi Khorasan, Iran; [Sarmad, Majid] Ferdowsi Univ Mashhad, Fac Math, Dept Stat, Mashhad, Razavi Khorasan, Iran
推荐引用方式 GB/T 7714	Farzandi, Mahboobeh,Sanaeinejad, Hossein,Rezaei-Pazhan, Hojat,et al. Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area[J],2021.
APA	Farzandi, Mahboobeh,Sanaeinejad, Hossein,Rezaei-Pazhan, Hojat,&Sarmad, Majid.(2021).Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area.ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY.
MLA	Farzandi, Mahboobeh,et al."Improving estimation of missing data in historical monthly precipitation by evolutionary methods in the semi-arid area".ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY (2021).