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Parametric Studies of ANFIS Family Capability for Thunderstorm Prediction

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Space Science and Communication for Sustainability

Abstract

Thunderstorms are an unpredictable natural hazard. They affect daily human life especially for space launcher development program in the near future. The variations in meteorological parameters were used to capture thunderstorm activity. In this study, six parameters, namely pressure, temperature, relative humidity, cloud, rainfall and precipitable water vapor were analyzed in order to develop a thunderstorm prediction system. To realize the development of this thunderstorm prediction system, we developed a thunderstorm prediction model based on the Adaptive Neuro-fuzzy Inference System (ANFIS) family (ANFIS FCM, ANFIS FSC, and ANFIS Human Expert). Three models from the ANFIS family were assessed to ascertain their capability for thunderstorm prediction. Input and output variables were taken from the Tawau meteorology station. The results showed that the thunderstorm prediction model based on ANFIS Human Expert showed a good efficiency with an estimated error prediction of <2% with root mean square error (RMSE) and percentage error (PE) values of 3.028% and 23.545% respectively compared to RMSE and PE of ANFIS FCM and ANFIS FSC.

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References

  1. Suparta W, Putro WS, Singh MSJ, Asillam MF (2015) Characterization of GPS and meteorological parameters for mesoscale convective systems model over Tawau, Malaysia. Adv Sci Lett 21:203–206

    Article  Google Scholar 

  2. Suparta W, Putro WS, Singh MSJ, Asillam MF (2015) The estimation of rainfall and precipitation variation during 2011 convective system using an artificial neural network over Tawau, Sabah. Paper presented at the 2015 IEEE international conference on space science and communication, space science centre (ANGKASA), Universiti Kebangsaan Malaysia, Langkawi, 10–12 August 2015

    Google Scholar 

  3. Chen TC, Wang SY, Yen MC (2007) Enhancement of afternoon thunderstorm activity by urbanization in a valley: Taipei. J Appl Meteorol Climatol 46:1324–1340

    Article  Google Scholar 

  4. Loo YY, Billa L, Singh A (2015) Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geosci Front 6:817–823

    Article  Google Scholar 

  5. Heistermann M, Crisologo I, Abon CC, Racoma BA, Jacobi S, Servando NT, David CPC, Bronstert A (2013) Using the new Philippine radar network to reconstruct the Habagat of August 2012 monsoon event around Metropolitan Manila. Nat Hazards Earth Syst Sci 13:653–657

    Article  Google Scholar 

  6. Albar AMH, AL-Khalaf AK, Mohamed HAB (2015) Radar rainfall estimation of a severe thunderstorm over Jeddah. Atmos Clim Sci 5:302

    Google Scholar 

  7. Velden C, Harper B, Wells F, Beven JL II (2006) The Dvorak tropical cyclone intensity estimation technique. Bull Am Meteorol Soc 87:1195

    Article  Google Scholar 

  8. Spiridonov V, Curic M (2015) A storm modeling system as an advanced tool in prediction of well organized slowly moving convective cloud system and early warning of severe weather risk. Asia Pac J Atmos Sci 51:61–75

    Article  Google Scholar 

  9. Litta AJ, Mary Idicula S, Mohanty UC (2013) Artificial neural network model in prediction of meteorological parameters during premonsoon thunderstorms. Int J Atmos Sci 1:1–14

    Google Scholar 

  10. Suykens JAK (2001) Nonlinear modeling and support vector machine. Paper presented at the 2001 IEEE instrumentation and measurement technology conference, Budapest convention centre, Budapest, Hungary, 21–23 May 2001

    Google Scholar 

  11. Rajasekaran S, Pai GV (2004) Neural networks. Fuzzy Logic and Genetic Algorithms. PEU Learning Pvt. Ltd, New Delhi, pp 17–456

    Google Scholar 

  12. Suparta W, Alhasa KM (2015) Modeling of zenith path delay over Antarctica using an adaptive neuro fuzzy inference system technique. Expert Syst Appl 42:1050–1064

    Article  Google Scholar 

  13. Cao Z, Kandel A, Li L (1990) A new model of fuzzy reasoning. Fuzzy Sets Syst 36:311–325

    Article  MATH  MathSciNet  Google Scholar 

  14. Stohlgren TJ, Chase TN, Pielke RA, Kittel TG, Baron J (1998) Evidence that local land use practices influence regional climate, vegetation, and stream flow patterns in adjacent natural areas. Glob Change Biol 4:495–504

    Article  Google Scholar 

  15. Xu X, Tang Q, Xia H, Zhang Y, Li W, Huo X (2016) Chaotic time series prediction for prenatal exposure to polychlorinated biphenyls in umbilical cord blood using the least squares SEATR model. Sci Rep 6:25005

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Ministry of Science, Technology and Innovation Malaysia (MOSTI) under Science Fund 01-01-02-SF1100 grant and partly funded by Flagship Program: ZF-2014-016 grant. The authors would like thank MetMalaysia, weather underground, NASA and Wyoming University for providing the meteorological data used in this study.

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Authors and Affiliations

  1. Dept of Electrical Engineering, Sanata Dharma University, 55282, Yogyakarta, Indonesia

    Wayan Suparta

  2. Space Science Centre (ANGKASA), Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Wayan Suparta & Wahyu Sasongko Putro

Authors
  1. Wayan Suparta
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  2. Wahyu Sasongko Putro
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Correspondence to Wayan Suparta .

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Editors and Affiliations

  1. Department of Electrical Engineering, Sanata Dharma University, Yogyakarta, Indonesia

    Wayan Suparta

  2. Institute of Climate Change, Space Science Centre, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Mardina Abdullah

  3. Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Mahamod Ismail

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Suparta, W., Putro, W.S. (2018). Parametric Studies of ANFIS Family Capability for Thunderstorm Prediction. In: Suparta, W., Abdullah, M., Ismail, M. (eds) Space Science and Communication for Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-10-6574-3_2

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  • DOI: https://doi.org/10.1007/978-981-10-6574-3_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6573-6

  • Online ISBN: 978-981-10-6574-3

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