Mathematics and Statistics Vol. 4(4), pp. 95 - 100
DOI: 10.13189/ms.2016.040401
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Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model

Nitaya Jantakoon *
Department of Applied Statistics, Faculty of Science and Technology, Rajabhat Maha Sarakham University, Thailand


The key atmospheric variables that impact crops are weather and rainfall. Extreme rainfall or drought at critical periods of a crop's development can have dramatic influences on productivity and yields. The analysis of effect of rainfall is needed to evaluate crop production in Northeastern Thailand. 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 and Root Mean Square Error (RMSE) were calculated, and on the basis of the results obtained, it can be suggested that fuzzy methodology is efficiently capable of handling scattered data.

Rainfall Prediction, Statistics Model, Meteorological Forecasting, Fuzzy Logic Model

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Nitaya Jantakoon , "Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model," Mathematics and Statistics, Vol. 4, No. 4, pp. 95 - 100, 2016. DOI: 10.13189/ms.2016.040401.

(b). APA Format:
Nitaya Jantakoon (2016). Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model. Mathematics and Statistics, 4(4), 95 - 100. DOI: 10.13189/ms.2016.040401.