摘要
【目的】构建冬小麦需水量预测模型,提高需水量预测的精准度,为基于气象信息的需水量预测提供更为可靠的方法。【方法】选取新疆奇台县近5年的气象数据,采用公式Penman-Monteith计算冬小麦需水量(近似为真实需水量),基于CNN-BiLSTM模型,将平均温度、风速、湿度和降水量4个变量作为输入参数,预测冬小麦需水量,对比评估预测CNN-BiLSTM与LSTM、BiLSTM等6种模型的精准性。【结果】采用少量参数分别输入BP、RNN、LSTM、改进的BiLSTM和CNN-BiLSTM等模型中预测需水量,BP神经网络的预测效果较差。在模型评估中,CNN-BiLSTM比LSTM的R 2提高约8%,MSE降低约0.56。【结论】CNN-BiLSTM模型对小麦需水量预测更加精准。
【Objective】Based on the meteorological data related to water demand forecasting of winter wheat,a water demand forecasting model with fewer parameters was constructed to improve the robustness of water demand forecasting,provides a more reliable method for forecasting water demand based on meteorological information.【Methods】Meteorological data of Qitai County in recent five years were selected,and the water requirement of winter wheat calculated by Penman-Monteith formula was approximately the real water requirement.Four variables including average temperature,wind speed,humidity and precipitation were taken as input parameters.The water requirement of winter wheat was forecasted,and the prediction of CNN-BiLSTM was compared with that of LSTM,BiLSTM and other 6 models.【Results】The results showed that when a few parameters were fed into BP,RNN,LSTM,improved BiLSTM and CNN-BiLSTM models to predict water demand,the prediction effect of BP neural network was poor.In the model evaluation,CNN-BiLSTM showed an R 2 improvement of about 14%over LSTM and a MSE reduction of about 3.8.【Conclusion】CNN-BiLSTM model is more accurate in predicting wheat water demand.
作者
杜云
张婧婧
雷嘉诚
李博
李永福
DU Yun;ZHANG Jingjing;LEI Jiacheng;LI Bo;LI Yongfu(College of Computer and Information Engineering,Xinjiang Agricultural University/Engineering Research Center of Intelligent Agriculture Ministry of Education/Xinjiang Agricultural Informatization Engineering Technology Research Center,Urumqi 830052,China;Institute of Soil Fertilizer and Agricultural Water Saving,Xinjiang Academy of Agricultural Sciences,Urumqi 830091,China)
出处
《新疆农业科学》
CAS
CSCD
北大核心
2024年第7期1590-1596,共7页
Xinjiang Agricultural Sciences
基金
新疆维吾尔自治区重大科技专项“农场数字化及智能化关键技术研究”(2022A02011-2)
科技创新2030—“新一代人工智能”重大项目(2022ZD0115805)。
