Atmospheric Temperature Prediction Based on a BiLSTM-Attention Model

Abstract

To address the problem that traditional models are not effective in predicting atmospheric temperature, this paper proposes an atmospheric temperature prediction model based on symmetric BiLSTM (bidirectional long short-term memory)-Attention model. Firstly, the meteorological data from five major stations in Beijing were integrated, cleaned, and normalized to build an atmospheric temperature prediction dataset containing multiple feature dimensions; then, a BiLSTM memory network was used to construct with forward and backward information in the time dimension. And the limitations of the traditional LSTM method in long-term time series analysis were solved by introducing the attention mechanism to achieve the prediction analysis of atmospheric temperature. Finally, by comparing the prediction results with those of BiLSTM, LSTM-Attention, and LSTM, it is revealed that the proposed model has the best prediction effect, with a MAE value of 0.013, which is 0.72%, 0.41%, and 1.24% lower than those of BiLSTM, LSTM-Attention, and LSTM, respectively; the R2 value reaches 0.9618, which is 2.73%, 1.23%, and 4.98% higher than BiLSTM, LSTM-Attention, and LSTM, respectively. The results show that the symmetrical BiLSTM-Attention atmospheric temperature prediction model can effectively improve the prediction accuracy of temperature data, and the model can also be used to predict other time series data.

Combining Recurrent Neural Network and Sigmoid Growth Models for Short-Term Temperature Forecasting and Tomato Growth Prediction in a Plastic Greenhouse

“…The above characteristics allow the network to capture information from past and future contexts, enhancing its ability to understand the temporal dependencies within the data. The BILSTM network structure is shown in Figure 4 and was implemented with the following equations [21]:…”

“…LSTM, GRU, or BILSTM [18,19,21], have exhibited satisfactory performances on temperature prediction. Considering the performance of each model, the LSTM2 with 190 hidden units was chosen as the best temperature forecasting model and had an R 2 of 0.962, MAPE of 3.2%, and RMSE of 1.2 °C (Table 2); its forecasting results were subsequently used to calculate the predicted GDD.…”

“…Previous research has demonstrated that ANNs have significant advantages in their flexibility to adapt to nonlinear and non-physical data-this characteristic provides ANNs with considerable potential for application to the environmental control of greenhouses [38]. ANNs on greenhouse microclimate prediction, such as multilayer perceptron [7,39], radial basis function [40], and RNN based on LSTM, GRU, or BILSTM [18,19,21], have exhibited satisfactory performances on temperature prediction. Considering the performance of each model, the LSTM2 with 190 hidden units was chosen as the best temperature forecasting model and had an R 2 of 0.962, MAPE of 3.2%, and RMSE of 1.2 • C (Table 2); its forecasting results were subsequently used to calculate the predicted GDD.…”

“…To overcome this problem, the long short-term memory (LSTM) [16] and the gated recurrent unit (GRU) [17] have been developed as the derivative model architectures of the RNN and are commonly used for indoor microclimate prediction [18][19][20]. Furthermore, Hao et al [21] used a bi-directional LSTM (BILSTM) to capture the time-series information in both the forward and the backward directions for atmospheric temperature prediction, which yielded good prediction results. Therefore, using RNNs, including the LSTM, GRU, and BILSTM networks, can provide effective solutions for temperature prediction in various applications.…”

An Entity of the Ocean Data Prediction System Using Machine Learning Techniques

“…Ranran, Luo, and others examine how machine learning is used in several applications, such as ocean component prediction, source identification and localization, and tracking of deep-sea resource availability. Here is a summary of current research and particular machine-learning techniques used on ocean data [7]. It discusses some of the study's shortcomings, possible uses, and future directions.…”