Advertisement

Application of Neuro-Fuzzy Techniques in the Estimation of Extreme Events

  • Mrinmoy MajumderEmail author
  • Rabindra Nath Barman
Chapter

Abstract

In hydroclimatic science, a hydrologic or climatic event like a flood or rainfall is said to be extreme if its occurrence is rare or the probability of its occurrence is below 5%. Predicting extreme events is a difficult task, and often conceptual models fail to perform optimally while predicting the time and frequency of extreme events. Due to this drawback, scientists are now opting for nature-based algorithms to make predictions about extreme events. The application of neural networks, along with the categorization ability of fuzzy logic, has been found to perform much better than conceptual models. The present study uses the same concept to develop a model that can predict the occurrence and frequency of extreme events with the help of a data set categorized by the application of fuzzy logic.

Keywords

Extreme events Neuro-fuzzy systems Combinatorial data matrix 

References

  1. Altunkaynaka A, Chellam S (2010) Prediction of specific permeate flux during crossflow microfiltration of polydispersed colloidal suspensions by fuzzy logic models. doi:http://dx.doi.org/10.1016/j.desal.2009.10.018
  2. Astel A (2006) Chemometrics based on fuzzy logic principles in environmental studies. doi:http://dx.doi.org/10.1016/j.talanta.2006.09.026
  3. Aziz SA, Parthiban J (1996) Fuzzy logic. Retrieved from http://www.doc.ic.ac.uk/∼nd/surprise_96/journal/vol4/sbaa/report.fuzzysets.html. Feb 2012
  4. Benlarbia K, Mokranib L, Nait-Said MS (2004) A fuzzy global efficiency optimization of a photovoltaic water pumping system. doi:http://dx.doi.org/10.1016/j.solener.2004.03.025
  5. Fayea RM, Sawadogoa S, Lishoua C, Camino FM (2002) Long-term fuzzy management of water resource systems. doi:http://dx.doi.org/10.1016/S0096-3003(02)00151-0
  6. Freissineta C, Erlicha M, Vauclin M (1998) A fuzzy logic-based approach to assess imprecisions of soil water contamination modeling. doi:http://dx.doi.org/10.1016/S0167-1987(98)00067-1
  7. Iijima T, Nakajimaa Y, Nishiwaki Y (1995) Application of fuzzy logic control system for reactor feed-water control. doi:http://dx.doi.org/10.1016/0165-0114(95)00036-K
  8. IPCC (2007) Climate change 2007: impacts, adaptation, and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson, CE (eds) Contribution of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 1000 ppGoogle Scholar
  9. Karabogaa D, Bagisb A, Haktanir T (2007) Controlling spillway gates of dams by using fuzzy logic controller with optimum rule number. doi:http://dx.doi.org/10.1016/j.asoc.2007.01.004
  10. Khalid A (2003) Adaptive fuzzy control of a water bath process with neural networks. doi:http://dx.doi.org/10.1016/0952-1976(94)90041-8
  11. LaMeres BJ, Nehrir MH, Gerez V (1999) Controlling the average residential electric water heater power demand using fuzzy logic. doi:http://dx.doi.org/10.1016/S0378-7796(99)00022-X
  12. Lcaga Y (2006) Fuzzy evaluation of water quality classification. doi:http://dx.doi.org/10.1016/j.ecolind.2006.08.002
  13. Lermontova A, Yokoyamab L, Lermontovc M, Augusta M, Machado S (2009) River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil. doi:http://dx.doi.org/10.1016/j.ecolind.2009.02.006
  14. Nehrir MH, LaMeres BJ (2000) A multiple-block fuzzy logic-based electric water heater demand-side management strategy for leveling distribution feeder demand profile. doi:http://dx.doi.org/10.1016/S0378-7796(00)00124-3
  15. Ocampo-Duquea W, Ferré-Huguetb N, Domingob JL, Schuhmachera M (2006) Assessing water quality in rivers with fuzzy inference systems: a case study. doi:http://dx.doi.org/10.1016/j.envint.2006.03.009
  16. Saruwatari N, Yomota A (2000) Forecasting system of irrigation water on paddy field by fuzzy theory. doi:http://dx.doi.org/10.1016/0378-3774(95)92338-F
  17. Schulz K, Huwe B (1998) Water flow modeling in the unsaturated zone with imprecise parameters using a fuzzy approach. doi:http://dx.doi.org/10.1016/S0022-1694(97)00038-3
  18. Sen Z, Altunkaynak A (2009) Fuzzy system modelling of drinking water consumption prediction. doi:http://dx.doi.org/10.1016/j.eswa.2009.04.028
  19. United States Environmental Protection Agency (2012) Extreme events, climate change-health and environmental effects. Retrieved from http://epa.gov/climatechange/effects/extreme.html. Feb 2012
  20. Yurduseva MA, Firat M (2008) Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: an application to Izmir, Turkey. doi:http://dx.doi.org/10.1016/j.jhydrol.2008.11.036

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Hydro-Informatics EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia
  2. 2.Department of Production EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia

Personalised recommendations