Research and application of CCLDBO-TCN-BiGRU-attention hybrid model for temperature prediction

Artificial intelligence algorithms not only process meteorological data, but their application in the field of meteorology has become an important means of improving forecast accuracy and expanding the dimension of analysis. Meteorological data present prominent spatio-temporal features and complex non-linear relationships, significantly challenging meteorological forecasting. Hence, to study the performance of hybrid models in weather prediction, this paper proposes a hybrid model named CCLDBO-TCN-BiGRU-Attention and experimentally compares it with current models. The proposed method improves the dung beetle optimization algorithm by introducing Circle mapping, the si-ne-cosine algorithm MSCA, and the dung beetle optimization algorithm using Levy flight (CCLDBO). The experimental results demonstrate that the developed method achieves outstanding results in a wide range of single-peak and multi-peak tests. Additionally, the evaluation index R2 reaches 0.9925 in the hybrid model for predicting the temperature in the Guizhong area, and the RMAE, RMSE, MSE, and MAPE metrics are the best compared with other models. The hybrid model is also closer to the real values than other models in predicting real value changes. Overall, the hybrid model performs well in temperature prediction and provides a feasible solution for weather prediction.