College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China.
Sensors (Basel). 2021 Jan 23;21(3):755. doi: 10.3390/s21030755.
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.
准确的短期小区域气象预报对于确保南极内陆作业和设备操作的安全至关重要。本研究提出了一种基于深度学习的多输入神经网络模型来解决这个问题。新提出的模型通过堆叠自动编码器和长短时记忆网络相结合进行预测。自堆叠自动编码器最大化了目标气象站传感器数据的特征并消除了冗余，并使用长短时记忆网络从传感器数据中提取时间特征。该新模型在不同的东南极纬度（包括南极最高点和沿海地区）的四个观测点评估了预测性能和泛化能力。通过五个评估指标比较了五个深度学习网络的性能，并讨论了最佳的输入组合形式。结果表明，该模型的预测能力优于其他模型。它为南极内陆小区域的短期气象预报提供了一种新方法。
