干旱区生态环境知识资源中心(Arid): Application of WNN-PSO model in drought prediction at crop growth stages: A case study of spring maize in semi-arid regions of northern China

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DOI	10.1016/j.compag.2022.107155
	Application of WNN-PSO model in drought prediction at crop growth stages: A case study of spring maize in semi-arid regions of northern China
	Cao Xiujia; Yin Guanghua; Gu Jian; Ma Ningning; Wang Zihao
通讯作者	Yin, GH ; Gu, J
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2022
卷号	199
英文摘要	Drought prediction of regional crops during the growth stages can get drought information in advance and prepare for the response, so as to effectively guide the water-saving irrigation and lessen yield losses of spring maize. This study used the daily meteorological data in the research area during 1965-2019 to calculate the Crop Water Deficit Index (CWDI) and used the Pearson correlation coefficient (PCC) method to select the relevant factors for the CWDI. Then, CWDI was predicted using Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), Wavelet Neural Network (WNN), Support Vector Machine-Particle Swarm Optimize (SVMPSO), Back Propagation Neural Network-Particle Swarm Optimize (BPNN-PSO), and Back Wavelet Neural Network-Particle Swarm Optimize (WNN-PSO) models. By comparing d, R-2, Root Mean Square Error (RMSE), and Mean Relative Error (MRE), the best model was selected and used to predict drought in the next five years. The compared results showed that WNN-PSO models performed better than all the other models. When the input variables were CWDI and 3 main relevant meteorological factors relative humidity, maximum temperature and precipitation at sowing-seedling stage, the MRE (1.58%similar to 6.65%), the MAE (1.2922 similar to 3.5866) as well as RMSE (0.0174 similar to 0.0481) were the smallest and the d(0.8608 similar to 0.915) and R-2 (0.8402 similar to 0.9853) was the largest. The model R-2 increased by 10.6%, 32.8% and 125.9% compared with WNN, BPNN-PSO and SVM-PSO. It is proved that WNN-PSO is suitable for predicting CWDI of spring maize in drought-affected areas.
英文关键词	Drought prediction Data driven models Wavelet neural network Spring maize Crop water deficit index
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000840712300003
WOS关键词	SUPPORT VECTOR REGRESSION ; ARTIFICIAL NEURAL-NETWORK ; CLIMATE INDEXES ; RIVER-BASIN ; SVM ; ANN ; PRECIPITATION ; ALGORITHM ; PROVINCE
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392184
推荐引用方式 GB/T 7714	Cao Xiujia,Yin Guanghua,Gu Jian,et al. Application of WNN-PSO model in drought prediction at crop growth stages: A case study of spring maize in semi-arid regions of northern China[J],2022,199.
APA	Cao Xiujia,Yin Guanghua,Gu Jian,Ma Ningning,&Wang Zihao.(2022).Application of WNN-PSO model in drought prediction at crop growth stages: A case study of spring maize in semi-arid regions of northern China.COMPUTERS AND ELECTRONICS IN AGRICULTURE,199.
MLA	Cao Xiujia,et al."Application of WNN-PSO model in drought prediction at crop growth stages: A case study of spring maize in semi-arid regions of northern China".COMPUTERS AND ELECTRONICS IN AGRICULTURE 199(2022).