Hybrid Climate Forecasting: Variational Mode Decomposition and Convolutional Neural Network with Long-Term Short Memory

Ozone (O3) pollution has surfaced as a significant threat to urban air quality in contemporary years. The precise and efficient forecast of ozone levels is fundamental in the mitigation and management of ozone pollution. Even though the air quality monitoring network offers useful multi-source pollutant concentration data for predicting ozone levels, existing models still grapple with issues arising from outlier and redundant sites influencing prediction precision, and cross-contamination between different pollutants. Also, the non-linear and volatile nature of monthly runoff makes accurate prediction more complex, provide a more granular and timely view of atmospheric flow variations. In this research, we introduce a hybrid model that unites Variational Modal Decomposition (VMD), particularly useful for separating mixed signals or extracting meaningful patterns from noisy or complex data, Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) is designed for processing sequences of data with grid-like structures, such as images or video frames. CNN-LSTMs use convolutional operations to capture spatial patterns and LSTM units to model temporal dependencies, making them effective for tasks like video analysis, image sequence prediction, and spatiotemporal data processing, and VMD-CNN-LSTM to counter these issues. We commence by deconstructing the historical data series from the Nanjing air quality monitoring stations using VMD. Then, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is applied to the VMD residual to acquire characteristic components or Intrinsic Mode Functions (IMFs). Each IMF is independently trained via LSTM to produce predictions for each component. Ultimately, we secure the final prediction by linearly superimposing the predictions from all components. The LSTM's adaptive learning ability and memory function make it ideal for managing long-term data, leading to more precise predictions. To evaluate the prediction performance on the test set, our VMD-CNN-LSTM model is compared with other models such as EMD-LSTM, EMD-CNN-LSTM, and VMD-LSTM using root mean square error (RMSE), mean absolute error (MAE), and Nash coefficient (NSE). Our findings reveal that the VMD-CNN-LSTM model surpasses the other models, displaying higher prediction precision and lower errors. Importantly, the model shows enhanced fitting of peak and valley values, thus providing a promising strategy for monthly runoff prediction. In this research, we've put forth a unique hybrid model, VMD-CNN-LSTM, for monthly ozone prediction. By amalgamating VMD, CNN, and LSTM, our model effectively tackles challenges associated with outlier and redundant sites, cross-pollution between pollutants, and nonlinearity makes it hard to model the intricate runoff relationships accurately, while instability results in unpredictable fluctuations, both of which impact the accuracy and reliability of monthly runoff predictions and make it more impactful in Environmental Management, Energy Optimization, Agriculture, Urban Planning, Climate Resilience