OPTIMIZATION FUZZY INFERENCE SYSTEM BASED PARTICLE SWARM OPTIMIZATION FOR ONSET PREDICTION OF THE RAINY SEASON
2021
Rainfall which is occurred in an area explain the Onset Rainy Season (ORS).
ORS is a characteristic of the rainy season which is important to know, but the
characteristics of the rain itself is very difficult to predict. We use the method of
Fuzzy Inference System (FIS) to predict ORS. Unfortunately, FIS is weak to
determine parameters so that influences the working FIS method. In this study,
we use PSO to optimize parameter of the FIS method to increase perform of
the FIS method for onset prediction of the rainy season with the predictor Sea
Surface Temperature Nino 3.4 and Index Ocean Dipole. We used coefficient
correlation to determine the relationship between two variables as predictors
and RMSE as evaluate to all methods. The experiment result has shown that
the work of FIS-PSO after optimizing produced the good work with the
coefficient correlation = 0.57 and RMSE = 2.96 that is the smallest value that
is better performance if compared with other methods. It can be concluded that
the method proposed can increase the onset prediction of the rainy season.
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