Shie-Yui Liong

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Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative(More)
We consider Markov chain Monte Carlo (MCMC) computational schemes intended to minimize the number of evaluations of the posterior distribution in Bayesian inference when the posterior is computationally expensive to evaluate. Our motivation is Bayesian calibration of computationally expensive computer models. An algorithm suggested previously in the(More)
The study presents two approaches to increase the generalization capability, or to overcome the over-fitting tendency, of neural networks so that their prediction accuracies for unseen data can be further enhanced. The use of early stopping and Bayesian regularization approaches are considered. Data used are the artificial Mackey-Glass time series and the(More)
Accurate rainfall intensity nowcasting has many applications such as flash flood defense and sewer management. Conventional computational intelligence tools do not take into account temporal information, and the series of rainfall is treated as continuous time series. Unfortunately, rainfall intensity is not a continuous time series as it has different dry(More)
Atmospheric aerosols influence precipitation by changing the earth's energy budget and cloud properties. A number of studies have reported correlations between aerosol properties and precipitation data. Despite previous research, it is still hard to quantify the overall effects that aerosols have on precipitation as multiple influencing factors such as(More)
Present paper consists the results from a study conducted to test the adequacy of artificial neural networks in modelling of dissolved oxygen (DO) in seawater. The input variables for ANN DO models are selected by statistical analysis. The ranking of important inputs and their mode of action on the output DO are obtained based on the expert’s opinion. The(More)
The classic Kalman filter implementation uses the measurements up to the time of forecast to update the initial conditions of the numerical model, with the updating effect limited to a prediction horizon when the improved initial conditions are washed out. To further enhance the prediction capability, this study proposes a new hybrid data assimilation(More)