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A coupled CEEMD-BiLSTM model for regional monthly temperature prediction.
Zhang, Xianqi; Xiao, Yimeng; Zhu, Guoyu; Shi, Jingwen.
Afiliación
  • Zhang X; Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
  • Xiao Y; Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou, 450046, China.
  • Zhu G; Technology Research Center of Water Conservancy and Marine Traffic Engineering, Henan Province, Zhengzhou, 450046, China.
  • Shi J; Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China. cicxym@163.com.
Environ Monit Assess ; 195(3): 379, 2023 Feb 09.
Article en En | MEDLINE | ID: mdl-36757488
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Desastres Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Desastres Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2023 Tipo del documento: Article País de afiliación: China