Abstract

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.

Highlights

  • Background and ObjectivesMeteorological forecasting is one of the most essential and demanding operational tasks carried out by meteoric services all over the world (Guhathakurata, 2006)

  • An accurate and timely rainfall forecast is crucial for reservoir operation and flooding prevention because it can provide an extension of lead-time of the flow forecast, larger than the response time of the watershed, in particular for small and medium-sized mountainous basins

  • Fuzzy set theory, as an alternate method has recently been applied to develop a model for predicting rainfall as it is concerned with ambiguities and vagueness (Ngamsantivong and Buntao (2013), Buntao and Kreinovich (2011a), Buntao and Kreinovich (2011b))

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Summary

Background and Objectives

Meteorological forecasting is one of the most essential and demanding operational tasks carried out by meteoric services all over the world (Guhathakurata, 2006). Statistical modeling is used to describe variability of quantities and errors in observations These models assume the observations to be numbers or vectors. This assumption is often not realistic because measurement results of continuous quantities are always not precise numbers but more or less non-precise. This kind of uncertainty is different from errors and variability. Karamouz et al (2004) used a model based on fuzzy rules and neural networks to predict rainfall in the western. Their results showed that both models had similar errors. The focus is on the development of fuzzy logic model for meteorological forecasting in the Northeastern Thailand using fuzzy logic approach

Description of Study Area
Fuzzy Logic
Fuzzy Logic Modeling of Meteorological Forecasting
Fuzzification
Defuzzification
Error Measures
Prediction Error
Acknowledgments and Legal Responsibility
Formula and Equation

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