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
  • Water supply systems
  • Smart water networks
  • Optimization


Current Projects

  • Dynamic deep learning for simultaneously forecasting hydrological variables across multiple timescales
  • 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 smart water network operations