RAINFALL VARIABILITY PREDICTION MODEL IN KUALA TERENGGANU, MALAYSIA USING PRINCIPAL COMPONENT ANALYSIS AND MULTIPLE LINEAR REGRESSION
DOI:
https://doi.org/10.11113/jurnalteknologi.v88.23248Keywords:
Climate Change, Principal Component Analysis, Multiple Linear Regression, Rainfall Prediction ModelAbstract
This study addresses the impact of climate change on Kuala Terengganu, Malaysia, focusing on rainfall variability prediction. As extreme weather events become more frequent, accurate climate forecasts are essential for effective disaster preparedness. The primary objective is to evaluate the effectiveness of Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) in predicting key climate indicators such as temperature, humidity, and precipitation. Using 2021 climate data, PCA was employed to identify significant variables influencing rainfall, which were then used in an MLR model to predict rainfall variability. The integrated PCA-MLR approach significantly improved prediction accuracy compared to MLR alone, identifying temperature, humidity, and wind speed as critical predictors. The study demonstrates that combining PCA and MLR enhances climate prediction accuracy, aiding better planning and response to climate challenges in Kuala Terengganu. This approach can improve disaster risk management and resilience. Future research should expand datasets and incorporate additional climate variables to refine predictive capabilities.
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