Home
About Me
PhD researcher developing hybrid models that combine process-based hydrology and machine learning to improve streamflow prediction and river temperature modelling across UK catchments.
I am a PhD researcher at the University of Birmingham working at the intersection of hydrological modelling, machine learning, and environmental data science. My research develops interpretable hybrid modelling frameworks that combine process-based hydrology with machine learning to improve streamflow prediction, reservoir-impacted catchment modelling, and river temperature forecasting.
I am particularly interested in building models that are not only accurate, but also physically meaningful, explainable, and useful for real-world water-resource and climate-adaptation challenges.
Research Vision
My research vision is to better understand and model hydrological systems by examining how process-based models, machine learning methods, and hybrid frameworks represent catchment behaviour under climate variability and human influence. My work focuses not only on improving prediction, but also on identifying which modelling approaches are most suitable for different catchment conditions, hydrological processes, and human-impacted systems. Through this, I aim to develop physically meaningful, interpretable, and robust modelling approaches that can support streamflow prediction, river temperature modelling, and wider water-resource decision-making.