Reasearch Interests
- Hydrological and water resources modelling, forecasting, and simulation
- Data-driven modelling (artificial intelligence, machine learning, deep learning, etc.)
- Data mining
- Time series analysis (e.g., wavelet transforms)
- Big Data applications in hydrology and water resources
- Uncertainty quantification and risk assessment
- Ensemble modelling/forecasting
- Data assimilation
- Drinking water distribution systems
- Optimization
Current Projects
- Dynamic deep learning for simultaneously forecasting hydrological variables across multiple timescales
- Improving the computational efficiency of deep learning models for probabilistic hydrological prediction
- Coupling process-based and data-driven models for improved hydrological prediction
- Application of wavelet transforms to hydrology and water resources with a focus on best practices
- An expert system for optimizing drinking water distribution systems operations
