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1. Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels.

2. A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction.

3. Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots.

4. Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms.