Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

•We study sea level anomaly in Singapore Regional Waters.•We improve hydrodynamic model prediction using data model integration.•An error forecasting method is applied to improve the hydrodynamic model.•Average mutual information is used to filter the selective parameters.•Genetic programming is used to model the sea level anomaly prediction errors.

With recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. Spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques have resulted in more favourable acceptance by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydro-informatics applications ranging from data mining for pattern discovery to data driven models and numerical model error correction. The present study explores the feasibility of applying mutual information theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e. assuming that the hydrodynamic model, available at this point, is best representation of the physics in the domain of interest) by relating them to variables that reflect the state at which the predictions are made such as input data, state variables and model output. In addition, the present study explores the possibility of employing ‘genetic programming’ (GP) as an offline data driven modelling tool to capture the sea level anomaly (SLA) dynamics and then using them for updating the numerical model prediction in real time applications. These results suggest that combination of data relationship analysis and GP models helps to improve the forecasting ability by providing information of significant predicative parameters. It is found that GP based SLA prediction error forecast model can provide significant improvement when applied as data assimilation schemes for updating the SLA prediction obtained from primary hydrodynamic models.