Development of Physics-Informed Data-Driven Storm Surge and Wave Models
This project develops next-generation modelling tools that combine machine learning and existing datasets to greatly improve the accuracy of storm surge and wave forecasts along Singapore’s coastlines. By harnessing advanced data-driven approaches, the research aims to enable storm surge predictions up to five days in advance, strengthening Singapore’s preparedness and response strategies against coastal hazards.
Research Goals
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Develop an advanced machine learning–based model for storm surge prediction.
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Enhance the accuracy and efficiency of forecasting along Singapore’s coastlines.
- Strengthen early warning capabilities to support coastal resilience and disaster preparedness.
Why This Matters
- Equips Singapore with the capability to predict storm surges up to five days in advance, improving preparedness and response to coastal hazards.
- Establishes a real-time data network that integrates local and global weather information for faster and more accurate storm surge predictions.
Latest Research Highlights
- Developed a tidal and wind-induced model using Delft3D, analyzing water levels and sea level anomalies to support numerical modeling of extreme events.
- Engineered key features from regional wind data correlated with storm surge occurrence, achieving high prediction accuracy using historical data.
- Developed a physics-informed data-driven model that predicts wave profiles by integrating physical equations with sensor data, significantly reducing prediction time through hyperparameter tuning.
Meet the Team
Meet the team behind the Development of Physics-Informed Data-Driven Storm Surge and Wave Models project.
Principal Investigator (PI):
Associate Professor Victor Wang
Singapore Institute of Technology
Co-Principal Investigator (Co-PI):
Assistant Professor Li Yuzhu, Pearl
National University of Singapore,
College of Design and Engineering
Co-Principal Investigator (Co-PI):
Associate Professor Tay Zhi Yung
Singapore Institute of Technology
Co-Principal Investigator (Co-PI):
Dr Ooi Seng Keat
National University of Singapore,
College of Design and Engineering,
Tropical Marine Science Institute and Technology Centre for Offshore and Marine (TMSI)
Co-Principal Investigator (Co-PI):
Associate Professor An Hui
Singapore Institute of Technology
Research Engineer:
Feng Zhijing
Singapore Institute of Technology
Co-Principal Investigator (Co-PI):
Assistant Professor Li Xiaorong
Singapore Institute of Technology
Research Engineer:
Ng Peng Shu
Singapore Institute of Technology
Co-Principal Investigator (Co-PI):
Assistant Professor Elisa Ang
Singapore Institute of Technology
More Projects from the H2 Domain
Discover research initiatives under the Monitoring, Prediction and Digitalisation of Coastal Environment domain that harness data and digital technologies to enhance Singapore’s resilience to sea-level rise, extreme weather, and coastal flooding.
Download CFI's project booklet for more information on each project
CFI Singapore Tranche 1 and 2
If you wish to reach out regarding a specific project, please email CFI Singapore at cfisg@nus.edu.sg