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Drought Monitoring: A Performance Investigation of Three Machine Learning Techniques

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Context-Aware Systems and Applications (ICCASA 2013)

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Abstract

This paper investigates the use of Soft Computing techniques on a drought monitoring case study. This is in effort to create an intelligent middleware for Ubiquitous Sensor Networks (USN) using machine learning techniques. Algorithms in Artificial Immune System, Neural Networks and Bayesian Networks were used. The paper reveals the results from an experiment on data collected over 95 years in the Trompsburg region of the Free State Province, South Africa.

The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-05939-6_37

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References

  1. Antonic, O., Krizan, J., Marki, A., Bukovec, D.: Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. Ecol. Modelling 138, 255–263 (2001)

    Article  Google Scholar 

  2. Antonio, S.C., Rafael, C., Carmen, S., Jose, M.G.: Bayesian networks for probabilistic weather prediction. In: Anonymous, pp. 695–700 (2002)

    Google Scholar 

  3. Dasgupta, D., Gonzales, F.: An immunity-based technique to characterize intrusions in computer networks. IEEE Trans. Evol. Comput. 6, 281–291 (2002)

    Article  Google Scholar 

  4. Douglas, A.P., Breilphol, A.M., Lee, F.N., Adapa, R.: The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Trans. Power Syst. 13, 1507–1513 (1998)

    Article  Google Scholar 

  5. Dunne, R.A.: A Statistical Approach to Neural Network for Pattern Recognition. Wiley-Interscience, New York (2007)

    Book  MATH  Google Scholar 

  6. Forrest, S., Hofmeyer, S.A., Somayaji, A., Longstaff, T.A.: A sense of self for Unix processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 120–128. Anonymous (1996)

    Google Scholar 

  7. Hayes, M.J.: Monitoring the 1996 drought using the standardized precipitation index. Bull. Am. Meteorol. Soc. 80, 429 (1999)

    Article  Google Scholar 

  8. Hughes, B.L., Saunders, M.A.: A drought climatology for Europe. Int. J. Climatol 22, 1571–1592 (2002)

    Article  Google Scholar 

  9. Jin, H., Jiang, W. (eds.): Handbook of Research on Developments and Trends in Wireless Sensor Networks: From Principle to Practice. Information Science Reference (IGI Global), Hershey (2010)

    Google Scholar 

  10. Kennett, R.J., Korb, K.B., Nicholson, A.E.: Seabreeze prediction using Bayesian networks. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 148–153. Springer, Heidelberg (2001)

    Google Scholar 

  11. Kjaerulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, 1st edn. Springer, New York (2007)

    MATH  Google Scholar 

  12. Lloyd-hughes, B., Saunders, M.A.: A drought climatology for Europe. Int. J. Climatol. 22, 1571–1592 (2002)

    Article  Google Scholar 

  13. Luther, K., Bye, R., Alpcan, T., Muller, A., Albayrak, S.: A Cooperative AIS framework for intrusion detection. In: IEEE International Conference on Communications. ICC ‘07, pp. 1409–1416. Anonymous (2007)

    Google Scholar 

  14. Masinde, M., Bagula, A.: A framework for predicting droughts in developing countries using sensor networks and mobile phones. In: Anonymous, pp. 390–393 (2010)

    Google Scholar 

  15. McKee, T.B., Doesken, N.J., Kleist, J.: The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference of Applied Climatology, 17–22 January 1993

    Google Scholar 

  16. McKee, T.B., Doesken, N.J., Kleist, J.: Drought monitoring with multiple time scales. In: 9th AMS Conference on Applied Climatology, pp. 233–236 (1995)

    Google Scholar 

  17. Mishra, A.K., Desai, V.R.: Drought forecasting using feed-forward recursive neural network. Ecol. Modelling 198, 127–138 (2006)

    Article  Google Scholar 

  18. Mishra, A.K., Singh, V.P.: A review of drought concepts. J. Hydrol. 391, 202–216 (2010)

    Article  Google Scholar 

  19. Sajikumar, N.: A non-linear rainfall-runoff model using an artificial neural network. J. Hydrol. 216, 32–35 (1999)

    Article  Google Scholar 

  20. Sims, A.P.: Adopting drought indices for estimating soil moisture: a North Carolina case study. Geophys. Res. Lett. 29, 1183 (2002)

    Article  Google Scholar 

  21. Watkins, A., Timmis, J., Boggess, L.: Artificial immune recognition system (AIRS): an immune-inspired supervised learning algorithm. Genet. Program. Evol. Mach. 5, 291–317 (2004)

    Article  Google Scholar 

  22. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  23. Xu, C., Li, T., Huang, X., Jiang, Y.: A weather forecast system based on artificial immune system. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 800–803. Springer, Heidelberg (2005)

    Google Scholar 

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Authors and Affiliations

  1. School of Computing, University of South Africa, Science Campus, Florida Park, Johannesburg, 1709, South Africa

    Pheeha Machaka

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  1. Pheeha Machaka
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Correspondence to Pheeha Machaka .

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Editors and Affiliations

  1. Faculty of Information Technology, Nguyen Tat Thanh University, Vietnam, Ho Chi Minh City, Vietnam

    Phan Cong Vinh

  2. Concordia University, Montreal, Canada

    Vangalur Alagar

  3. University of Limerick, Limerick, Ireland

    Emil Vassev

  4. University of Allahabad, Allahabad, India

    Ashish Khare

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© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Machaka, P. (2014). Drought Monitoring: A Performance Investigation of Three Machine Learning Techniques. In: Vinh, P., Alagar, V., Vassev, E., Khare, A. (eds) Context-Aware Systems and Applications. ICCASA 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-05939-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-05939-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05938-9

  • Online ISBN: 978-3-319-05939-6

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