Development of a Fuzzy Logic Based Rainfall Prediction Model

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Author(s) Agboola A.H. | Gabriel A. J. | Aliyu E.O. | Alese B.K.
Pages 427-435
Volume 3
Issue 4
Date April, 2013
Keywords Fuzzy rule-based, Knowledge base, Fuzzy reasoning, Fuzzification, Defuzzification.

Abstract

The present study investigates the ability of fuzzy rules/logic in modeling rainfall for South Western Nigeria. The developed Fuzzy Logic model is made up of two functional components; the knowledge base and the fuzzy reasoning or decision-making unit. Two operations were performed on the Fuzzy Logic model; the fuzzification operation and defuzzification operation. The model predicted outputs were compared with the actual rainfall data. Simulation results reveal that predicted results are in good agreement with measured data. Prediction Error, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the Prediction Accuracy were calculated, and on the basis of the results obtained, it can be suggested that fuzzy methodology is efficiently capable of handling scattered data. The developed fuzzy rule-based model shows flexibility and ability in modeling an ill-defined relationship between input and output variables.

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