Interpretable Machine Learning Modelling of Future Extreme Floods under Climate Change

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Machine learning (ML) is a powerful technique that has rapidly found many applications across numerous disciplines. Estimating climate change impact on future extreme events such as super floods and heatwaves has been traditionally carried out using physically and process-based hydro-climatic models; however, ML-based modelling has become a new focal area thanks to its ability to account for the highly complex, nonlinear physical and social-physical interactions in this context. A huge challenge that still remains in ML modelling for future extreme floods, is its 'black-box' nature where the interactions among various components are hardly explainable, which hinders its effective application in supporting critical decision-making processes to mitigating climate change.This PhD project aims to address these challenges by developing a set of ML modelling tool kits with improved interpretability and transparency for predicting future risks of extreme floods under the impact of climate change. The expected outcome of the project will offer an accountable and reliable modelling support for critical decision making on mitigation measures against extreme floods under the changing climate. The PhD student involved will be supported by an experienced supervision team consisting of academics, industrial partners as well as frontline practitioners with expertise in HPC-based hydro-climatic modelling, AI and machine learning. The PhD student will also be benefitted from the systematic training in modelling and AI, career development offered by the host university via it's the Zienkiewicz Research Institute under the theme of Modelling, Data and AI in Climate Action.