Method of Bidirectional LSTM Modelling for the Atmospheric Temperature

Shuo Peng Liang, Dingcheng Wang, Jingrong Wu, Rui Wang, Ruiqi Wang

Intelligent Automation & Soft Computing · 01 January 2021

In one sentence. BL-FC, a bidirectional LSTM with fully connected layers, forecasts hourly station temperature in Ireland and beats LSTM, GRU and RNN, especially for abnormal temperatures.

Abstract

Atmospheric temperature forecast plays an important role in weather forecast and has a significant impact on human daily and economic life. However, due to the complexity and uncertainty of the atmospheric system, exploring advanced forecasting methods to improve the accuracy of meteorological prediction has always been a research topic for scientists. With the continuous improvement of computer performance and data acquisition technology, meteorological data has gained explosive growth, which creates the necessary hardware support conditions for more accurate weather forecast. The more accurate forecast results need advanced weather forecast methods suitable for hardware. Therefore, this paper proposes a deep learning model called BL-FC based on Bidirectional Long Short-Term Memory (Bi-LSTM) Network for temperature modeling and forecasting, which is suitable for big data processing. BL-FC consists of four layers: the first layer is a Bi-LSTM layer, which is used to learn features from continuous temperature data in forward and backward directions; the other three layers are fully connected layers, the second and third layers are used to further extract data features, and the last layer is used to map the final output of temperature prediction. Based on the meteorological data of 19822 consecutive hours provided by Belmalit Mayo Weather Station in Mayo County, Ireland, the data set is established by using the sliding window method. Compared with other three different deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN), the BL-FC model has higher short-term temperature prediction accuracy, especially in the case of abnormal temperature.

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