AI & Data-Driven Environmental Modeling

Machine learning, optimization, and hydrological modelling supporting environmental prediction, system design, and intelligent decision-making.

Data-Driven Environmental Research

Treatment-System Optimization

Adaptive control, incremental learning, and genetic optimization are used to improve the performance and responsiveness of wastewater-treatment systems.

Hydrological Modelling

Remote sensing, statistical modelling, and wavelet analysis are applied to river-flow dynamics and climate-hydrology relationships.

Decision Support

The work supports more informed decisions about drought monitoring, flow management, monitoring-network design, and environmental risk.

Practical Value

Data-driven methods complement field and laboratory research by helping organizations interpret complex environmental data and respond to changing system conditions.

Related publications and research profile — University of CalgaryRead the AI environmental-optimization project
Industry Problem

Environmental systems generate complex datasets difficult to optimize manually.
Research Approach

Adaptive control, incremental learning, genetic optimization, and predictive modelling for environmental engineering and water-resource systems.
Industry Impact

Improved operational insight, treatment-system optimization, and adaptive water- resource decision-making.

Data-Driven Environmental Solutions

Partner to develop smarter environmental systems.

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