基于CNN-LSTM模型的全球气温预测研究

Research on Global Temperature Prediction Based on CNN-LSTM Model

最新数据表明,自20世纪初以来,温室效应不断加剧,导致全球平均气温上升约1.4℃,极端高温天气严重影响了人们的生活、生产和健康。因此,对全球气温进行预测具有重要意义,本文根据气温时间序列构建ARIMA自回归时间序列预测模型和深度卷积长短期记忆网络模型(CNN-LSTM)对未来20年的全球年平均气温进行预测。为了对比CNN-LSTM模型和ARIMA模型的预测效果,我们分别利用1880年至2022年的全球平均气温数据对这两种模型进行了训练和预测。通过对预测结果的对比和精度验证,可以全面评估这两种模型在气温预测方面的表现。研究结果表明,CNN-LSTM模型在预测精度和稳定性方面优于ARIMA模型,CNN-LSTM模型结合了卷积神经网络(CNN)和长短期记忆网络(LSTM)的优点,CNN能够降低数据维度,而LSTM能保持对长时间跨度的时间序列的良好记忆。这种模型充分考虑了气象数据的时间相关性,从而可以提高对海量、长时间序列气温数据的预测精度。

The latest data shows that since the beginning of the 20th century, the greenhouse effect has been intensifying, resulting in a rise in the global average temperature of about 1.4˚C, and extreme heat weather has seriously affected people’s lives, production and health. Therefore, it is of great signifi-cance to predict global temperature, and this paper constructs an ARIMA autoregressive time se-ries prediction model and a deep convolutional long short-term memory network model (CNN-LSTM) based on the temperature time series to predict the global annual average temperature in the next 20 years. In order to compare the prediction performance of the CNN-LSTM model and the ARIMA model, we trained and predicted the two models using global average temperature data from 1880 to 2022, respectively. By comparing the prediction results and verifying the accuracy, the perfor-mance of the two models in temperature prediction can be comprehensively evaluated. The results show that the CNN-LSTM model is superior to the ARIMA model in terms of prediction accuracy and stability, and the CNN-LSTM model combines the advantages of convolutional neural network (CNN) and long short-term memory network (LSTM). CNN can reduce the data dimension, while LSTM can maintain a good memory of the time series with a long span, and this model fully considers the temporal correlation of meteorological data, so as to improve the prediction accuracy of massive and long-term temperature series data.