Abstract
Time varying coefficient ARMA and ARMA-transfer function (ARMA-TF) models for improving forecasting of water quality and runoff are presented and applied. Kalman filter algorithms are used for recursively updating the coefficients in the models. In the Kalman filter algorithm, different schemes are used for estimating the noise covariance matrices R and Q. Their performances in tracking the variability of the coefficients are also examined. The methodology is then applied to obtain one-step-ahead water quality and runoff forecasts in Hong Kong. It was found that forecasting improvements can be achieved by using the time varying coefficient ARMA and ARMA-TF modelling approach with a Kalman filter algorithm.
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Jayawardena, A.W. (1996). Adaptivity in Stochastic Modelling and Forecasting. In: Singh, V.P., Kumar, B. (eds) Proceedings of the International Conference on Hydrology and Water Resources, New Delhi, India, December 1993. Water Science and Technology Library, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0389-3_28
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DOI: https://doi.org/10.1007/978-94-011-0389-3_28
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