基于LSTM深度学习模型的华北地区参考作物蒸散量预测研究

为有效提高华北地下水漏斗区参考作物蒸散量ET_0的预报精度,本文以华北地区7个气象代表站1958—2010年ET_(0-PM)(Penman-Monteith,P-M)的历史时间序列为训练集构建LSTM模型,以2011—2017年ET_(0-PM)的时间序列为验证集将LSTM模型与其他4种经验模型进行对比分析。结果表明:LSTM在华北地区预测的整体评价指标Gpi(Global performance indicator)排名第一,该模型可以作为华北地区逐月ET_0预测的推荐模型,为我国精准农业灌溉预报提供科学的依据。

Study on LSTM deep learning model-based prediction of reference crop evapotranspiration in North China

In order to effectively improve the accuracy of prediction on the reference crop evapotranspiration ( ET0) in groundwater funnel area of North China， the LSTM ( Long Short-Term Memory) model is constructed through taking the historical time series of ET0 － PM( Penman-Monteith，P － M) from 1958 to 2010 from seven meteorological stations in North China as the training set，and then a comparative analysis between LSTM model and the other four empirical models is made by taking the time series of ET0 － PM from 2011 to 2017 as the validation set. The result shows that the global performance indicator ( Gpi) of LSTM prediction in North China ranks number one， thus the model can be used as the recommended model for ET0 prediction in North China and provide a scientific basis for precise agricultural irrigation prediction in China.