Development of physics-informed data-driven storm surge and wave models
PI: Associate Professor Victor Wang
What is this about?
This project aims to develop advanced modelling tools that integrate machine learning (ML) with existing data to significantly enhance the accuracy of storm surge and wave forecasts along Singapore’s coastlines.
What is the goal?
Build a physics-informed, ML-based model that accurately and efficiently predicts storm surges along Singapore’s coastlines.
How is this done?
Analyse weather data likely to influence storm surges.
Perform detailed feature design and engineering to identify predictors of storm surge occurrences.
Select and deploy the most suitable ML model that balances prediction speed with accuracy.
Test the model rigorously against a range of simulated and historical storm surge and weather data to ensure its reliability and accuracy.
Why does this matter?
Equips Singapore with the capability to predict storm surges with up to a five-day lead time — enhancing preparedness and response strategies.
Enables the formation of a network that utilises real-time local and global weather data to deliver quick and accurate predictions of storm surge heights.
