深度学习方法在农业产业园区气象要素预报中的应用

Application of Deep Learning Method in Meteorological Facts Forecast of Agricultural Industrial Parks

不同区域农业产业园区的精细化气象要素预报是实现农业现代化的重要保障,开展能普遍适用于不同地区农业产业园区的气象要素预报技术有重要的应用价值。将深度学习方法应用于农业产业园区的气象要素预报中,利用深度学习方法建立了预报气象要素的神经网络订正模型。结果表明,经过神经网络模型订正后,气象要素预报误差大幅度减小。

Fine forecast of meteorological facts in different regional agricultural industrial parks is an important guarantee for realizing agricultural modernization. It has important application value to develop the meteorological facts forecast technology which can be widely applied to agricultural industrial parks in different areas. In this paper, the deep learning method was applied to the forecast of meteorological facts in agricultural industrial parks, and a modified neural network model for forecasting meteorological facts was established by deep learning method. The results showed that the prediction error of meteorological facts was greatly reduced after the neural network model was revised.