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Comparing typhoon intensity prediction with two different artificial intelligence models

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Abstract

In typhoon-prone areas, in order to announce catastrophe warnings in advance accurate storm-intensity prediction is essential. In this study, forecasting typhoon intensity with their characteristics has been supported by two different artificial intelligence methods, a simple artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) based on genetic algorithm (GA). The data used throughout this paper are from the reanalysis data set of the National Centers for Environmental Prediction and the best track database of the National Oceanic and Atmospheric Administration. The typhoons selected for this study happened during 1985–2008 over the South China Sea. By comparing the results from these two methods, their advantages and disadvantages were investigated. The results of the study confirm the superiority of the ANFIS-GA method, because its root mean square error was 3.78, which is significantly lower than the value of 6.11 for the ANN method in the best experiments. Considering the literature in typhoon intensity prediction by ANN and ANFIS models, the contribution of this paper is to investigate whether or not these methods are able to forecast typhoon intensity. The study represents that the ANFIS model can produce more applicable predictions for typhoon intensity than the ANN model.

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References

  • Ali M, Kishtawal C, Jain S (2007) Predicting cyclone tracks in the north Indian Ocean: an artificial neural network approach. Geophys Res Lett 34(4):L04603

    Article  Google Scholar 

  • Banik S, Chanchary FH, Khan K, Rouf RA, Anwer M (2008) Neural network and genetic algorithm approaches for forecasting bangladeshi monsoon rainfall. In: Computer and Information Technology. ICCIT 2008. 11th International Conference on, 2008. IEEE, pp 735–740

  • Bao J, Michelson S, Wilczak J (2002) Sensitivity of numerical simulations to parameterizations of roughness for surface heat fluxes at high winds over the sea. Mon Weather Rev 130(7):1926–1932

    Article  Google Scholar 

  • Chang LC, Chang FJ (2001) Intelligent control for modelling of real-time reservoir operation. Hydrolo Proc 15(9):1621–1634

    Article  Google Scholar 

  • Chau K, Wu C, Li Y (2005) Comparison of several flood forecasting models in Yangtze River. J Hydrolo Eng 10(6):485–491

    Article  Google Scholar 

  • Chaudhuri S, Middey A (2011) Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms. Meteor Atmos Phys 114(3):139–149

    Article  Google Scholar 

  • EIA (2012) South China Sea [Online]. Available: http://www.eia.gov/countries/regions-topics.cfm?fips=SCS. Accessed 1 May 2012

  • Gao Y, Wu T, Chen B, Wang J, Liu Y (2013) A numerical simulation of microphysical structure of cloud associated with the 2008 winter freezing rain over Southern China. J Meteorolo Soc-JPN 91(2):101–117

    Article  Google Scholar 

  • Haghroosta T, Ismail WR (2013) Influence of noul typhoon on Sea surface temperature, heat fluxes and precipitation rate. Int J Scient Res Publ 3(6):1–14

    Google Scholar 

  • Jacovides C, Kontoyiannis H (1995) Statistical procedures for the evaluation of evapotranspiration computing models. Agr Water Mgmt 27(3):365–371

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. Syst Man Cybern IEEE Trans 23(3):665–685

    Article  Google Scholar 

  • Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406

    Article  Google Scholar 

  • Jang JR, Sun C, Mizutani E (1997) Neuro-fuzzy and soft computing. PTR Prentice Hall, New Jersey

    Google Scholar 

  • Jeong C, Shin JY, Kim T, Heo JH (2012) Monthly precipitation forecasting with a neuro-fuzzy model. Water Resour Mgmt 26(15):4467–4483

    Article  Google Scholar 

  • Jin L, Yao C, Huang X (2006) An improved method on meteorological prediction modeling using genetic algorithm and artificial neural network. In: The Sixth World Congress on Intelligent Control and Automation (WCICA), 2006, Dalian, China. IEEE, pp 31–35

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Met Soc 77(3):437–471

    Article  Google Scholar 

  • Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) The international best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data. Bull Am Met Soc 91(3):363–376

    Article  Google Scholar 

  • Lin GF, Chen LH (2005) Application of an artificial neural network to typhoon rainfall forecasting. Hydrolo Proc 19(9):1825–1837

    Article  Google Scholar 

  • Liu M (2006) Discrete-time delayed standard neural network model and its application. Sci China Ser F-Earth Sci 49(2):137–154

    MATH  Google Scholar 

  • Ma J, Hasi B (2005) Remote sensing data classification using tolerant rough set and neural networks. Sci China Ser D-Earth Sci 48(12):2251–2259

    Article  Google Scholar 

  • Majumdar KK (2002) A Fuzzy dynamical system modelling of a disturbance leading to cyclogenesis. J Intel Fuzzy Syst 13(1):7–15

    Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  MathSciNet  MATH  Google Scholar 

  • More A, Deo M (2003) Forecasting wind with neural networks. Mar Struct 16(1):35–49

    Article  Google Scholar 

  • Pallavi VP, Vaithiyanathan V (2013) Combined artificial neural network and genetic algorithm for cloud classification. Int J Eng Tech 5(2):787–794

    Google Scholar 

  • Pan J, Yan X-H, Jo Y-H, Zheng Q, Liu WT (2004) A new method for estimation of the sensible heat flux under unstable conditions using satellite vector winds. J Phys Oceanogr 34(4):968–977

    Article  Google Scholar 

  • Rumelhart DE, Widrow B, Lehr MA (1994) The basic ideas in neural networks. Commun ACM 37(3):87–92

    Article  Google Scholar 

  • Sivanandam M (2009) Introduction to artificial neural networks. Vikas Publishing House Pvt Ltd, India

    Google Scholar 

  • Smith M (1993) Neural networks for statistical modeling. John Wiley & Sons Inc, New York

    MATH  Google Scholar 

  • Sudheer K, Gosain A, Ramasastri K (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrolo Proc 16(6):1325–1330

    Article  Google Scholar 

  • Sugeno M, Kang G (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28(1):15–33

    Article  MathSciNet  MATH  Google Scholar 

  • Tseng CM, Jan CD, Wang JS, Wang C (2007) Application of artificial neural networks in typhoon surge forecasting. Ocean Eng 34(11):1757–1768

    Article  Google Scholar 

  • Wang Y, Zhang W, Fu W (2011) Back Propogation (BP)-neural network for tropical cyclone track forecast. In: 19th International Conference on Geoinformatics, Shanghai, China. IEEE, pp 1–4

  • Wong C, Venneker R, Uhlenbrook S, Jamil A, Zhou Y (2009) Variability of rainfall in Peninsular Malaysia. Hydrol Earth Syst Sci Discuss 6:5471–5503

    Article  Google Scholar 

  • Yao C, Jin L, Huang M, Huang X (2007) An experiment with methods of forecasting tropical cyclones intensity base on combination of the genetic algorithm and artificial neural network. Acta Oceanolo Sinica 04:456–457

    Google Scholar 

  • Yip C, Wong K (2004) Efficient and effective tropical cyclone eye fix using genetic algorithms. Knowl Based Intel Inf Eng Syst 3213:654–660

    Google Scholar 

  • Zhang GJ, McPhaden MJ (1995) The relationship between sea surface temperature and latent heat flux in the equatorial Pacific. J Climate 8(3):589–605

    Article  Google Scholar 

  • Zhou X, Chen JC (2006) Ensemble forecasting of tropical cyclone motion using a baroclinic model. Adv Atmos Sci 23(3):342–354

    Article  Google Scholar 

  • Zurada JM (1992) Introduction to artificial neural systems, vol 4. PWS Publishing C, Boston

    Google Scholar 

Download references

Acknowledgments

This study is supported by a fellowship from Center for Marine and Coastal Studies (CEMACS), Universiti Sains Malaysia.

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Authors and Affiliations

  1. Center for Marine and Coastal Studies (CEMACS), Universiti Sains Malaysia, 11800, Minden, Pulau Pinang, Malaysia

    Tahereh Haghroosta

  2. Section of Geography, School of Humanities, Universiti Sains Malaysia, 11800, Minden, Pulau Pinang, Malaysia

    Wan Ruslan Ismail

  3. Centre for Global Sustainability Studies, Universiti Sains Malaysia, 11800, Minden, Pulau Pinang, Malaysia

    Wan Ruslan Ismail

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  1. Tahereh Haghroosta
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  2. Wan Ruslan Ismail
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Correspondence to Tahereh Haghroosta.

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Haghroosta, T., Ismail, W.R. Comparing typhoon intensity prediction with two different artificial intelligence models. Evolving Systems 6, 177–185 (2015). https://doi.org/10.1007/s12530-014-9106-0

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  • DOI: https://doi.org/10.1007/s12530-014-9106-0

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