The Comparison of Long Short-Term Memory Neural Network and Deep Forest for the Evaporation Duct Height Prediction
Qixiang Liao, Yanbo Mai, Zheng Sheng, Yuhui Wang, Qingjian Ni, Shudao Zhou
Abstract
An evaporation duct is a type of atmospheric stratification that affects radio systems. Atmospheric duct prediction is helpful for radar detection. In this article, we used the deep forest, which is different from a deep learning framework, to predict the atmospheric duct height. At the same time, the long short-term memory (LSTM) neural network and other machine learning algorithms, such as the logistic regression (LR), random forest (RF), Bayes, and support vector regression (SVR) algorithms, were adopted to predict the evaporation duct height (EDH). The predicted results with filled and unfilled missing data show that an accurate prediction of the EDH can be achieved using the deep forest.