Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou, 450046, China.
Environ Monit Assess. 2023 Feb 9;195(3):379. doi: 10.1007/s10661-023-10977-5.
Temperature is an important indicator of climate change. With the gradual increase of global warming, a well-chosen model can improve the accuracy of temperature prediction. It is of great significance and value for future disaster prevention and mitigation and economic development. Monthly temperature is influenced by solar activity, monsoon, and other factors, with significant uncertainty, ambiguity, and randomness. A coupled CEEMD-BiLSTM temperature model is constructed based on the good decomposition-reconstruction characteristics of CEEMD for uncertain time series and the advantages of BiLSTM for solving stochastic prediction, and it is applied to the prediction of monthly temperature in Zhengzhou City. The results show that the minimum relative error of the coupled CEEMD-BiLSTM model is 0.01%, the maximum relative error is 0.99%, and the average relative error is 0.22%, and the prediction accuracy of this coupled model for monthly temperature in Zhengzhou is higher than that of the CEEMD-LSTM model, EEMD-BiLSTM model, and BP neural network model, with better stability and adaptability.
温度是气候变化的一个重要指标。随着全球变暖的逐渐加剧，一个精心选择的模型可以提高温度预测的准确性。对于未来的灾害预防和减轻以及经济发展来说，这具有重要的意义和价值。月平均温度受到太阳活动、季风等因素的影响，具有显著的不确定性、模糊性和随机性。基于 CEEMD 对不确定时间序列的良好分解-重构特性和 BiLSTM 解决随机预测的优势，构建了一个耦合的 CEEMD-BiLSTM 温度模型，并将其应用于郑州市月平均温度的预测中。结果表明，该耦合模型的最小相对误差为 0.01%，最大相对误差为 0.99%，平均相对误差为 0.22%，该耦合模型对郑州市月平均温度的预测精度高于 CEEMD-LSTM 模型、EEMD-BiLSTM 模型和 BP 神经网络模型，具有更好的稳定性和适应性。
