Author(s): Alamsyah Kurniawan; Seng Keat Ooi; Vladan Babovic
Keywords: Tidal and non-tidal barotropic numerical model; Data mining; Average mutual information; Singapore Regional Waters
Abstract: The application of ocean-atmosphere coupling through tidal and non-tidal barotropic numerical modelling to forecast sea level in Singapore Regional Waters have greatly improved the understanding of the factors and mechanisms influencing of sea level in Singapore Strait. However, complex governing mechanisms, multi-scale, multi-dimensional, time varying, and highly non-linear dynamics of the marine systems make the oceanographic modelling efforts much more challenging. Hence, there is an increasing need for alternate approaches which can provide vital information leading to better domain knowledge and reduced time and effort required to tune the numerical models. With increasing spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques are becoming more favourable and acceptable to the hydrodynamic community. Data mining tools and techniques are being applied in variety of hydro-informatics applications ranging from simple 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 of tidal and non-tidal barotropic numerical modelling by relating them to variables that reflect the physics such as spatial distribution of tidal constituents and tidesurge interaction. The findings introduce the novel application on understanding the non-tidal representation of the numerical model.