文献类型： 外文期刊

第一作者： Cao Xiujia

作者： Cao Xiujia;Yin Guanghua;Gu Jian;Ma Ningning;Wang Zihao;Cao Xiujia;Wang Zihao;Gu Jian

作者机构：

关键词： Drought prediction; Data driven models; Wavelet neural network; Spring maize; Crop water deficit index

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 199 卷

页码：

收录情况： SCI

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.

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