Abstract
The regression is a causal forecasting method that fits curves to the entire data set to minimize the forecasting errors. It should be noted that the linear statistic-based regression models does not support nonlinear in forecasting. According to literature, Bayesian- and Neural Network-based regression for seasonal typhoon activity forecasting is more effective than the traditional regression models. In this paper, a conjunct space cluster-based adaptive neuro-fuzzy inference system (ANFIS) is applied for seasonal forecasting of tropical cyclones making landfall along the Vietnam coast. The experimental results indicated that the conjunct space cluster-based ANFIS for seasonal forecasting of tropical cyclones is an effective approach with high accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Azizi, A., Ali, Y.A., Ping, L.W.: Model Development and Comparative Study of Bayesian and ANFIS Inferences for Uncertain Variables of Production Line in Tile Industry. Wseas Transactions on Systems 11(1), 22–37 (2012)
Chan, J.C.L.: Tropical cyclone activity in the Northwest Pacific in relation to the El Nino/Southern Oscillation phenomenon. Mon. Wea. Rev. 113, 599–606 (1985)
Chan, J.C.L.: Interannual variability of tropical cyclones making landfall over China. ESCAP/WMO Typhoon Committee Annual Review Rev., 85–94 (1993)
Chu, P.-S.: Bayesian Forecasting of Seasonal Typhoon Activity: A Track-Pattern-Oriented Categorization Approach. Journal of Climate 23, 6654–6668 (2010)
Chu, P.-S., Zhao, X.: Bayesian change-point analysis of tropical cyclone activity: The central North Pacific case. J. Climate 17, 4893–4901 (2004)
Chu, P.-S., Zhao, X.: A Bayesian regression approach for predicting seasonal tropical cyclone activity over the central North Pacific. J. Climate 15, 4002–4013 (2007)
Chu, P.S., et al.: Climate Prediction of Tropical Cyclone Activity in the Vicinity of Taiwan Using the Multivariate Least Absolute Deviation Regression Method. Terr. Atmos. Ocean. Sci. 18(4), 805–825 (2007)
Dong, K.: El Nino and tropical frequency in the Australian region and the northwest Pacific. Australian Meteor. Mag. 36, 219–225 (1988)
Fan, K., Wang, H.: A New Approach to Forecasting Typhoon Frequency over the Western North Pacific (2009)
Goh, A.Z.C., Chan, J.C.L.: Variations and prediction of the annual number of tropical cyclones affecting Korea and Japan. Int. J. Climatology (2010a)
Goh, A.Z.C., Chan, J.C.L.: Interannual and interdecadal variations of tropical cyclone activity in the South China Sea. International Journal of Climatology 30, 827–843 (2010b)
Goh, A.Z.C., Chan, J.C.L.: An Improved Statistical Scheme for the Prediction of Tropical Cyclones Making Landfall in South China (2010c)
Gray, W.M., et al.: Atlantic seasonal hurricane frequency: Part I: El Nino and 30mb quasi-biennial oscillation influences. Mon. Wea. Rev. 112, 1649–1668 (1984)
Gray, W.M., et al.: Predicting Atlantic seasonal Hurricane activity 6-11 Months in Advance. Weather and forecasting 7, 440–455 (1992)
Kim, H.S., et al.: Seasonal prediction of summertime tropical cyclone activity over the East China Sea using the least absolute deviation regression and the Poisson regression. Int. J. Climatology. 30, 210–219 (2010)
Landsea, C.W.: El Niño-Southern Oscillation and the seasonal predictability of tropical cyclones. In: Diaz, H.F., Markgraf, V. (eds.) El Niño: Impacts of Multiscale Variability on Natural Ecosystems and Society (2000) (in press)
Nguyen, S.D., Ngo, K.N.: An adaptive input data space parting solution to the synthesis of neuro-fuzzy models. Int. J. Control Autom. Syst. 6, 928–938 (2008)
Nicholls, N.: A possible method for predicting seasonal tropical cyclone activity in the Australian region. Mon. Wea. Rev. 107, 1221–1224 (1979)
Nicholls, N.: Recent performance of a method for forecasting Australian seasonal tropical cyclone activity. Aust. Mete. Mag. 40, 105–110 (1992)
Panella, M., Rizzi, A., Frattale Mascioli, F.M., Martinelli, G.: ANFIS synthesis by hyperplane clustering. In: Proc. of IFSA/NAFIPS, Vancouver, Canada, vol. 1, pp. 340–345 (2001)
Panella, M., Gallo, A.S.: An input-output clustering approach to the synthesis of ANFIS networks. IEEE T. Fuzzy Systems 13(1), 69–81 (2005, 2012)
Shapiro, L.J.: The relationship of the quasi-biennial oscillation to Atlantic tropical storm activity. Mon. Wea. Rev. 117, 1545–1552 (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Duong, T.H., Nguyen, D.C., Nguyen, S.D., Hoang, M.H. (2013). An Adaptive Neuro-Fuzzy Inference System for Seasonal Forecasting of Tropical Cyclones Making Landfall along the Vietnam Coast. In: Nguyen, N., van Do, T., le Thi, H. (eds) Advanced Computational Methods for Knowledge Engineering. Studies in Computational Intelligence, vol 479. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00293-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-00293-4_17
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00292-7
Online ISBN: 978-3-319-00293-4
eBook Packages: EngineeringEngineering (R0)