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Dr. Md. Munir Hayet Khan, Professor

Faculty of Engineering and Quantity Surveying, INTI International University, Nilai, Malaysia

Biography: Dr. Md. Munir Hayet Khan is Professor of Civil and Environmental Engineering at INTI International University, Malaysia. He holds a PhD in Civil and Structural Engineering and an MSc in Environmental Engineering, and is a Professional Technologist, DOE-registered EIA consultant, CPESC, and Graduate Member of the Institution of Civil Engineers. His research spans hydrology and water resources, hybrid AI forecasting, climate resilience, river morphology, environmental modelling, and IoT-enabled monitoring. He has authored more than 90 scholarly publications, supervised multiple postgraduate research, led funded environmental projects, and received the 2026 ASCE-EWRI Visiting International Fellowship and an AGU Outstanding Reviewer recognition. His work emphasizes translating intelligent modelling and field sensing into practical decisions for sustainable water and environmental management.

Topic: From Data to Decisions: Hybrid AI, IoT and Digital Twins for Climate-Resilient Water and Environmental Systems

Abstract: Climate change, urbanization, and environmental degradation are increasing the frequency and complexity of floods, droughts, water-quality deterioration, sedimentation, and pollution. Conventional monitoring and modelling approaches often struggle to convert fragmented observations into timely, reliable decisions. This keynote presents an integrated framework that combines Internet of Things sensing, geospatial information, hybrid artificial intelligence, and digital-twin concepts for climate-resilient water and environmental management. Drawing on applications in hydrological drought forecasting, streamflow prediction, river morphology and sediment monitoring, and air- and water-quality assessment, the presentation shows how signal decomposition, machine learning, deep learning, and process-informed modelling can improve prediction while retaining physical meaning. It will discuss real-time data pipelines, multi-source data fusion, uncertainty and explainability, model transferability, and the importance of field validation. Particular attention will be given to moving beyond accuracy-only comparisons toward decision-oriented systems that support early warning, adaptive operation, and transparent environmental governance. The keynote concludes with a practical research roadmap for trustworthy, scalable, and regionally relevant intelligent environmental systems, including opportunities for cross-country collaboration in Asia.