Elsevier

Computers & Geosciences

Volume 72, November 2014, Pages 94-104
Computers & Geosciences

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

https://doi.org/10.1016/j.cageo.2014.07.007Get rights and content

Highlights

  • 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.

Abstract

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.

Introduction

The understanding of the generating mechanisms and the ability to generate forecasts of tidal and non-tidal flow phenomena (also referred to sea level anomaly) and their forcing mechanisms for the highly complex Singapore Regional Waters are of both scientific and economic importance. Given its geographically constricted location, the island of Singapore, part of the Sunda Shelf, experiences a direct impact of nonlinear dynamical interactions between the South China Sea, Andaman Sea and Java Sea. The complex shallow water hydrodynamics generated due to multiple ocean currents moving into and out of its the region, combined with short term meteorological effects leads to a very high variability of the sea water level around Singapore׳s coast. In such narrow straits separating larger water bodies, it is often observed that the water currents and levels deviate significantly from their regular tidal behaviour. These deviations or residual components are generally not accounted for during ocean weather forecasting and hence seriously affect coastal planning and navigation in the region. Hence, analysis and accurate prediction of these sea level anomaly (SLA) and current anomaly becomes an important part of oceanographic modelling, especially in of such shallow water zones. A major step in their analysis and forecasting is the development of an accurate hydrodynamic model to predict the non-tidal barotropic water levels and currents in the region (Ooi et al., 2009, Ooi et al., 2011, Kurniawan et al., 2013). 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. Conventional numerical models provide primary solution to this challenging task of characterizing and forecasting ocean weather (mainly water level and flow) by representing the underlying physics in terms of solvable equations. Yet, capturing the ocean dynamics in totality, accounting for the non-tidal anomaly calls for rigorous tuning of the models for further improvement. Such an exercise demands detailed domain knowledge and heavy computational effort. 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 the recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. Increasing spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques are becoming more favourable and acceptable by the hydrodynamic community. The 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 (Babovic et al., 2001, Babovic, 2009, Sannasiraj et al., 2005, Sun et al., 2010, Rao and Babovic, 2010, Karri et al., 2013, Karri et al., 2014, Wang and Babovic, 2014). The objectives of this paper is to explore the feasibility of applying average mutual information (AMI) 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 by Kurniawan et al. (2013), best represents the physics in the domain of interest given all the available data) 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 also explores the possibility of employing ‘genetic programming’ (GP) as an offline data driven modelling tool to capture the SLA dynamics and then using them for updating the numerical model prediction in real time applications. To the best knowledge of the authors, no study has been carried out to update the numerical model SLA prediction using combination AMI and GP.

Section snippets

Data availability in the study area

The Singapore Regional Waters is defined as the area between 95°E–110°E and 6°S–11°N. It encompasses the two strategic waterways Malacca Strait and Singapore Strait, the central part of the shallow Sunda Shelf which connects the South China Sea (SCS) and the Java Sea, and part of the deep basin of the Andaman Sea. The present work is based on observations data used by Rao and Babovic (2010) and model predictions made in the year 2004 used by Kurniawan et al., 2011, Kurniawan et al., 2013. Table

Data relationship analysis

The data analysis conducted with the observed data and sea level anomaly (SLA) model simulation for the year 2004 has revealed important points regarding the time dynamics and length scales involved in the interaction between the SLA data observed at various locations around the Singapore Regional Waters, the interaction between meteorological variables and the observed SLA and SLA prediction errors. Fig. 4 shows temporal distribution of autocorrelation and AMI showing analysis of individual

Conclusions and recommendations

Data relationship analysis has been carried to evaluate the content, flow and time dynamics of information regarding the observed sea level anomaly (SLA) and its prediction errors with respect to a number of selected parameters. The analysis has been done both for the observed SLA and the SLA prediction errors. The analysis helps to establish how much information contained in the SLA prediction errors can be traced back to some of the variables. The significance test is done using average

Acknowledgements

The authors gratefully acknowledge the support and contributions of the Singapore-Delft Water Alliance (SDWA) and Deltares Strategic research funding. The research presented in this work was carried out as part of the SDWA׳s “Must-Have Box” research program (R-264-001-003-272).

References (27)

  • V. Babovic et al.

    Evolutionary computing in hydrology

  • R.G. Gallager

    Information Theory and Reliable Comminication

    (1968)
  • Gerritsen, H., Twigt, D., Mynett, A., Calkoen, C., and Babovic, V. Analysis and prediction of sea level anomalies and...
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