Conjunction Weighted Average Method with Fuzzy Expert System for Weather Event Forecasting – A Monthly Outlook

U Ramya Devi, K Uma

Abstract


Fuzzy logic as a limiting case of approximate reasoning is viewed in exact reasoning, consider everything in a matter of degree. A collection of elastic or equivalently interpreted to knowledge, a collection of variables in fuzzy constraint. Inference is process as a propagation of elastic constraints. Every logical system is fuzzified in fuzzy logic. Fuzzy logic is fascinating area of research, it trading off between significance and precision. It is convenient way to map space of input to a space of output. Fuzzy logic as so far as the laws of Mathematics refers to reality, they are not certain and so far, as they are certain as complexity rises, precise statements lose meaning and meaningful statements lose precision. Most meteorological infrastructure is surprisingly versatile. For example, the same radar system that can detect oncoming storms will also be useful for gathering general rainfall data for the farming sector. Being able to predict and forecast the weather also allows for data to be gathered to build up a more detailed picture of a nation’s climate, and trends within it

Keywords


Fuzzy logic, rainfall and weather forecasting

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References


Zadeh, L.A., 1965. Fuzzy Sets Information and Control, pp: 338 – 353.

Kosko, B., 1992. Neural networks and Fuzzy systems. Prentice Hall. Englewood Cliffs, N.J.

Abraham, A., Philip N. and Joseph B. (2001); “Soft Computing Models for Long Term Rainfall Forecasting”: In: 15th European Simulation Multi conference (ESM, August/September 2001), Modeling and Simulation 2000, Kerckhoffs, E.J.H. and M. Snorek (Eds.). Czech Republic, Prague, pp: 1044 – 1048.

Hari S. and Saravanan, R.“Short term electric load prediction Using Fuzzy BP”, Journal of Computing and Information Technology, Vol. 3, 2007, pp.1 – 15

Bardossy, A., Duckstein L. and Bogardi I. (1995); “Fuzzy rule-based classification of atmospheric circulation patterns”: Int. J. Climatol., 15: 1087 – 1097.

Hong T, “Short- term Electric load forecasting”, PHD Thesis, Graduate Faculty of North Carolina State University, October, 2010, pp. 1 – 175.

Edvin and Yudha (2008); “Application of Multivariate ANFIS for Daily Rainfall Prediction: Influences of Training Data Size”: Makara, Sains, Volume 12, No. 1, April 2008: 7 – 14

Swaroop, R. and. Hussein, A. A. “Load forecasting for power system planning using fuzzy-neural network”, Proceeding of the World Congress on Engineering and Computer Science, San Fransico, USA, Vol. 1, October 24-26, 2012, pp. 1-5.

Wong, K.W., Wong P.M., Gedeon T.D. and Fung C.C. (2003); “Rainfall prediction model using soft computing technique”: Soft Comput. Fusion Foundat. Methodol. Appli.7:434 – 438.

Özelkan, E.C., Ni F. and Duckstein L. (1996); “Relationship between monthly atmospheric circulation patterns and precipitation: Fuzzy logic and regression approaches”: Water Resour. Res., 32: 2097 – 2103.




DOI: http://dx.doi.org/10.23755/rm.v44i0.928

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Ratio Mathematica - Journal of Mathematics, Statistics, and Applications. ISSN 1592-7415; e-ISSN 2282-8214.