Extracting fuzzy rules and parameters using particle swarm optimization for rainfall forecasting

This paper deals with rainfall forecasting using rainfall data which taken from Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) and Oceanic Niño Index (ONI) data from NOAA Satellite and Information Service for Karangploso district. This paper proposes a Fuzzy Takagi-Sugeno-Kang rules and parameters extracting from Particle Swarm Optimization (PSO) for rainfall forecasting. The novel of fuzzy rules and parameters extracting from PSO is used to obtain the rules and parameters within the data. Therefore, we able to obtain better accuracy. The experiment results demonstrate that the proposed solution able to obtain better accuracy. These results have proved the robustness of the proposed solution.