CAO Xiu-jia,GU Jian,MA Ning-ning,et al.基于粒子群优化小波神经网络模型的春玉米生育阶段干旱预测[J].灌溉排水学报,0,():-.
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曹秀佳1, 谷健2, 马宁宁2, 刘泳圻3, 王子豪2, 尹光华2
1.中国科学院沈阳应用生态研究所 沈阳;2.中国科学院沈阳应用生态研究所;3.沈阳农业大学
关键词:  干旱预测;小波神经网络;春玉米;作物水分亏缺指数;模型
基金项目:“十三五”国家重点研发计划项目(2017YFD0300704,2018YFD0300301);辽宁省自然科学( 20180550617);中国博士后(2018M641708);
Drought Forecasting in Different Growth Stages of Spring Maize Using Particle Swarm Optimization-Wavelet Neural Network Model
CAO Xiu-jia,GU Jian,MA Ning-ning,et al
1.Institute of Applied Ecology,Chinese Academy of Sciences;2.Shenyang Agricultural University
【Background】Drought is one of the meteorological disasters in China and has occurred frequently In recent years. The area affected by droughts accounts for the largest proportion of meteorological disasters, which has a huge impact on society, economy and ecology. Drought in Northeast is more frequent, longer lasting, and more severe. In the context of global warming, increasing temperature and decreasing precipitation will cause droughts to occur more frequently and severely. Therefore, drought forecasting has become an important research topic in agricultural production.【Objective】This paper aimed to carry out drought forecast and guide the efficient water-saving supplemental irrigation production of spring maize better. 【Method】The Pearson correlation coefficient method was used to select the factors most relevant to the drought index among daily meteorological data of Fumeng County, Fuxin City from 1965 to 2019. The crop water deficit index at different growth stages of spring maize was forecasted using particle swarm optimization-wavelet neural network model (PSO-WNN). 【Result】The results show that, root mean square error (RMSE) are 0.0419、0.0174、0.0481、0.0297、0.0421, and the determination coefficients (R2) are 0.8402、0.9853、0.8990、0.9575、0.9177 respectively of the five growth stages (sowing-seedling, seedling-joining, jointing-tasseling, tasseling-milking, milking-maturity), and the forecast results are consistent with the actual drought levels. There may be no drought or slight drought in the sowing-seedling stage of the drought in this region, moderate or even extreme drought may occur in the seedling-jointing stage, and the degree of drought in the later growth stage Gradually weakening, the two stages of jointing-tasseling and tasseling-milking have a higher probability of slight drought, and the probability of drought in the milking-maturity stage is lower and less severe. 【Conclusion】This paper proves that the model is suitable for drought forecasting in Fuxin area. And more attention should be paid to drought of maize in the seedling-jointing stage in this region in the next five years.
Key words:  Drought forecasting; wavelet neural network; spring maize; crop water deficit index; model