With the recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. With increasing spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques are becoming more favorable and acceptable to the hydrodynamic community. The data model integration tools and techniques are being applied in variety of hydroinformatics applications ranging from simple data mining for pattern discovery to data driven models and numerical model error correction. The present study explores the possibility of employing “genetic programming” (GP) as an offline data driven modelling tool to capture the sea level anomalies (SLA) dynamics in Singapore Regional Waters (SRW) and then using them for updating the numerical model prediction in real time applications. In the final stage 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. The results have shown a good performance of non-tidal barotropic numerical modelling and GP error forecast model to forecast the SLA at Singapore Strait.
Kurniawan, Alamsyah; Ooi, Seng Keat; and Babovic, Vladan, "Improved Sea Level Anomaly Prediction Through Genetic Programming In Singapore Regional Waters" (2014). CUNY Academic Works.