A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica

• Published in: Sensors
• Main Authors: Yuchen Wang, Yinke Dou, Wangxiao Yang, Jingxue Guo, Xiaomin Chang, Minghu Ding, Xueyuan Tang
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: neural network; East Antarctica; multi-sensor; LSTM; Chemical technology; TP1-1185; Antarctic; The Antarctic; Antarc*; Antarctica
• Online Access: https://doi.org/10.3390/s21030755; https://doaj.org/article/adb7b1eafa094e0f98d2b2da9f891555

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station’s sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.