干旱区生态环境知识资源中心(Arid): A novel hybrid dragonfly optimization algorithm for agricultural drought prediction

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DOI	10.1007/s00477-021-02011-2
	A novel hybrid dragonfly optimization algorithm for agricultural drought prediction
	Aghelpour, Pouya; Mohammadi, Babak; Mehdizadeh, Saeid; Bahrami-Pichaghchi, Hadigheh; Duan, Zheng
通讯作者	Aghelpour, P (corresponding author), Bu Ali Sina Univ, Dept Water Engn, Fac Agr, Hamadan, Hamadan, Iran.
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN	1436-3240
EISSN	1436-3259
出版年	2021
英文摘要	Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM's parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM's accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases.
英文关键词	Palmer drought severity index Drought prediction Dragonfly algorithm Stochastic model Iran
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000637635300001
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352460
作者单位	[Aghelpour, Pouya] Bu Ali Sina Univ, Dept Water Engn, Fac Agr, Hamadan, Hamadan, Iran; [Mohammadi, Babak; Duan, Zheng] Lund Univ, Dept Phys Geog & Ecosyst Sci, Solvegatan 12, SE-22362 Lund, Sweden; [Mehdizadeh, Saeid] Urmia Univ, Water Engn Dept, Orumiyeh, Iran; [Bahrami-Pichaghchi, Hadigheh] Sari Agr Sci & Nat Resources Univ, Dept Water Engn, Fac Agr Engn, Sari, Iran
推荐引用方式 GB/T 7714	Aghelpour, Pouya,Mohammadi, Babak,Mehdizadeh, Saeid,et al. A novel hybrid dragonfly optimization algorithm for agricultural drought prediction[J],2021.
APA	Aghelpour, Pouya,Mohammadi, Babak,Mehdizadeh, Saeid,Bahrami-Pichaghchi, Hadigheh,&Duan, Zheng.(2021).A novel hybrid dragonfly optimization algorithm for agricultural drought prediction.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT.
MLA	Aghelpour, Pouya,et al."A novel hybrid dragonfly optimization algorithm for agricultural drought prediction".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2021).