Treatment-process optimization
Machine-learning and optimization approaches can help identify operating conditions that improve treatment effectiveness and resource efficiency.
Project Case Study
AI and Environmental Optimization
Data-driven modelling and optimization approaches that support better environmental-system performance and decisions.
Making environmental data useful
Hydrological and environmental modelling
The research includes data-driven methods for hydrological systems, remote sensing, and environmental prediction.
Adaptive learning
Incremental learning and evolutionary optimization methods support analysis when environmental systems change over time or contain complex patterns.
Applied decision support
From data to action
The aim is not only to model a system, but to provide useful evidence for treatment design, monitoring, planning, and operational decisions.
Integrated environmental research
Data science is applied alongside water treatment, contaminant removal, and environmental monitoring to help answer practical questions.
Related research
Explore the wider AI and data-driven environmental-modelling research area.
Explore a data-driven research collaboration
We welcome projects that combine environmental expertise, operational data, and practical implementation needs.
Propose a Challenge