Advertisement

Fuzzy Spatial Forecasting Model of Rainfall Distribution for Flood Early Warning

  • Mahmod OthmanEmail author
  • Siti Nor Fathihah Azahari
  • Noor Atiqah Abu Massuut
Conference paper

Abstract

Flood is commonly known as one of the most frequent type of natural disaster worldwide that occurred when the ground is not able to absorb and accommodate the heavy rainfall. Flood might be caused by increased levels of river water more than the river bank or the dams. Many methods have been proposed to forecast the rainfall distribution but mostly the forecasting accuracy of the existing method are questionable. In this paper, a fuzzy time series method was proposed to forecast the rainfall distribution. The objectives of this paper, first is to formulate fuzzy spatial forecasting model for rainfall distribution for each month in Perlis. Second, to predict the accurate rainfall values in future for early warning of flood in order to reduce flood issues. Using the fuzzy spatial forecasting method, the historical data of rainfall in Perlis were used to forecast. After that, several rules was applied to determine whether the rainfall forecasting trend value goes downward or upward movement Then, the mean square error (MSE) was calculated to compare the forecasting rainfall results of various forecasting method. The smaller the value of MSE, the better the forecasting model. The monthly historical rainfall distribution in Perlis for 4 years had been used to illustrate the forecasting algorithm of the new fuzzy time series method. The experimental results of this research exhibited higher forecasting accuracy for forecasting rainfall compare to existing methods.

Keywords

Flood Fuzzy forecasting Rainfall distribution 

Notes

Acknowledgments

The study was funded by “Long Term Research Grant (LRGS) (UUM/RIMPC/P-30)” and the authors also thank the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA for providing the laboratory facilities for completing the study.

References

  1. Bernama (2011) More people evacuated in flood-hit Perlis. Borneo Post http://www.theborneopost.com/2011/04/02/more-people-evacuated-in-flood-hit-perlis/
  2. Bol’shev LN (2001) Encyclopedia of mathematic. Statistical estimator. http://www.encyclopediaofmath.org/index.php/Statistical_estimator
  3. Ceballos A, Martinez-Fernandez J, Luengo-Ugidos MA (2004) Analysis of rainfall trends and dry periods on pluviometric gradient representative of Mediterranean climate in the Duero Basin, Spain. J Arid Env 58:215–233Google Scholar
  4. Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10:209–222CrossRefGoogle Scholar
  5. Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81:311–319CrossRefGoogle Scholar
  6. Chen SM, Hsu CC (2004) A new method to forecast enrollments using fuzzy time series. Int J Appl Sci Eng 2:234–244Google Scholar
  7. Chen SM, Hwang JR (2000) Temperature prediction using fuzzy time series. IEEE Trans Syst Man Cybern Part B: Cybern 30:263–275CrossRefGoogle Scholar
  8. El-Shafie AH, El-Shafie A, El Mazoghi HG, Shehata A, Taha MR (2011) Artificial neural network techniques for rainfall forecasting applied to Alexandria, Egypt. Int J Phys Sci 6(6):1306–1316Google Scholar
  9. Flood List (2014) About flood. http://floodlist.com/aboutfloods
  10. Hung NQ, Babel MS, Weesakul S, Tripathi NK (2008) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci Discuss 5:183–218CrossRefGoogle Scholar
  11. Hwang JR, Chen SM, Lee CH (1998) Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst 100:217–228CrossRefGoogle Scholar
  12. Lazim M, Alias (2011) Introductory business forecasting a practical approach, 3rd edn. University Publication Center (UPENA), Shah AlamGoogle Scholar
  13. Ngongondo C, Xu C, Gottschalk L (2011) Evaluation of spatial and temporal characteristics of rainfall in Malawi. A case of data scarce region. Theor Apply Climatol 106:79–93CrossRefGoogle Scholar
  14. Song Q (2003) A note on fuzzy time series model selection with sample autocorrelation functions. Cybern Syst Int J 34:93–107CrossRefGoogle Scholar
  15. Song Q, Chissom BS (1994) Forecasting enrolmentswith fuzzy time series—part 2. Fuzzy Sets Syst 62:1–8CrossRefGoogle Scholar
  16. The official portal for department of irrigation and drainage Malaysia(2013). Flood management—program activities. Definition of flood. http://www.water.gov.my/our-services-mainmenu-252/flood-mitigation-mainmenu-323/programme-aamp-activities-mainmenu-199?lang=en
  17. Tymvios F, Savvidou K, Michaelides SC (2010) Association of geo potential height patterns with heavy rainfall events in Cyprus meteorological services, Nicosia, Cyprus. J Refereed Proc Spec Publ 23:73–78Google Scholar
  18. Wackerly D, Mendenhall W, Acheaffier RL (2007) Mathematical statistics with application, 7th edn. Brooks/Cole Cengage Learning, USAGoogle Scholar
  19. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefGoogle Scholar
  20. Zaw WT, Naing TT (2009) Modelling of rainfall prediction over Myanmar using polynomial regression. Int Conf Comput Eng Technol 1:316–320Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Mahmod Othman
    • 1
    Email author
  • Siti Nor Fathihah Azahari
    • 1
  • Noor Atiqah Abu Massuut
    • 1
  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAArauMalaysia

Personalised recommendations