Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

Abstract

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.

Implementation of Particle Swarm Optimization (PSO) to Improve Neural Network Performance in Univariate Time Series Prediction

“…As time series prediction research has progressed, many studies have been presented that use combined methods to predict time series in order to optimize the performance of their proposed model [9]. One of the popular algorithms used to optimize traditional time series prediction models is particle swarm optimization (PSO) [11] [12]. Particle Swarm Optimization (PSO) was proposed by Kennedy and Eberhart in 1995, and is based on the social behaviors of birds and fish during the predatory process.…”

Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City

“…The extreme weather conditions that occur in the city of Mataram can be seen in the rainfall data for 2014 to 2018 and experience fluctuating conditions over a certain period of time making it difficult to forcast the weather accurately (Pascawati et al 2019). Therefore, accurate weather information based on scientific studies is very important as a reference (Noviandi and Ilham 2020). This information can be in the form of forecasts of rainfall both in the short term and in the long term.…”

“…This information can be in the form of forecasts of rainfall both in the short term and in the long term. Forecasting the amount of rainfall is very important (Wichitarapongsakun et al 2016;Dhamodharavadhani and Rathipriya 2019;Noviandi and Ilham 2020) information because it can be used to plan several production sectors such as agricultural production (Hartomo, Subanar, and Winarko 2015), plantations (Zhu et al 2020), fisheries (Dunstan et al 2018), aviation (Chen and Wang 2019), public service (Golding et al 2019), and so on. In addition, this information is useful for early detection of disasters that may occur due to extreme rainfall (Zhu et al 2020).…”