El Niño can affect climate patterns, causing extreme weather events, such as floods and droughts, around the world. Accurate forecasting of El Niño events allows preparation for El Niño-related disasters. However, the performance of current methods for predicting El Niño events one year in advance is not effective. This study proposes a hybrid approach to predicting the El Niño-related Oceanic Niño Index (ONI) and El Niño events with a lead time of 12 months. The proposed approach combines the convolutional Long Short-Term Memory (LSTM) Encoder-Decoder model with the Empirical Mode Decomposition (EMD) technique. The EMD technique can decompose time series data into a set of Intrinsic Mode Functions and a residue. Subsequently, the convolutional LSTM Encoder-Decoder model is employed to make an independent prediction for each component. Finally, the predicted data from each model can be reconstructed to obtain the forecasting results. The proposed approach is applied to the monthly ONI dataset for 1950–2019. The prediction model is trained and validated on the historical ONI values from 1950 to 2007 and forecasts El Niño events over a period of 12 years (2008–2019) with a lead time of 12 months. The results demonstrate that the proposed approach can successfully forecast that, for this period, 2009–2010, 2015–2016, and 2018–2019 are El Niño years. The performance of the proposed approach is then assessed by comparing it with the standalone convolutional LSTM Encoder-Decoder model, the LSTM-based models, and machine learning algorithms. The evaluated results indicate that the proposed approach outperforms these models in ONI predictions and El Niño event forecasts for a lead time of 12 months.

厄尔尼诺现象会影响气候模式，在世界范围内引发极端天气事件，例如洪水和干旱。准确预测厄尔尼诺事件可为与厄尔尼诺有关的灾难做好准备。但是，目前的方法无法提前一年预测厄尔尼诺事件。这项研究提出了一种混合方法来预测与厄尔尼诺有关的海洋性尼诺指数（ONI）和厄尔尼诺事件，前置时间为12个月。所提出的方法将卷积长短期存储器（LSTM）编码器-解码器模型与经验模式分解（EMD）技术结合在一起。EMD技术可以将时间序列数据分解为一组固有模式函数和残差。随后，采用卷积LSTM编码器/解码器模型对每个分量进行独立预测。最后，可以重构每个模型的预测数据以获得预测结果。提议的方法适用于1950-2019年的每月ONI数据集。该预测模型是根据1950年至2007年的ONI历史值进行训练和验证的，并预测12年（2008-2019年）期间的厄尔尼诺事件，前置时间为12个月。结果表明，所提出的方法可以成功预测在此期间，2009-2010年，2015-2016年和2018-2019年是厄尔尼诺现象。然后，通过将其与独立的卷积LSTM编码器/解码器模型，基于LSTM的模型和机器学习算法进行比较，来评估所提出方法的性能。