Weather radar echo prediction method based on convolution neural network and Long Short-Term memory networks for sustainable e-agriculture

Abstract

As one of the main reference data of weather forecast, weather radar echo image is very important to the stability of agricultural production. Different radar echo patterns represent different disastrous weather, such as hail, severe convection, and so on. Weather radar echo shape prediction can help meteorologists to judge the future changes of disastrous weather, help to avoid the harm of extreme weather to agriculture, and minimize agricultural economic losses. With the application of deep learning in the meteorological field, the deep learning method shows great potential in radar echo prediction. However, there are few research methods to predict the change of weather radar echo shape. This paper presents a radar echo prediction method based on c Convolutional Neural Networks and Long Short-Term Memory networks, which can effectively predict the shape of weather radar echo. The actual data is used to train and test our model. Experiments show that the model can accurately predict the change of echo shape. The quantitative evaluation of the model uses detection probability, false alarm rate, critical success index, and heidke skill score. The average scores predicted by this model in 1.5 h were 0.8223,0.2012,0.6812 and 0.7564, respectively, which were better than those predicted by ConvLSTM and TrajGRU models. The qualitative and quantitative results verify the effectiveness of the model, which shows that the model can be effectively applied to the actual weather forecast and improve the stability of agricultural production.