A STUDY ON AIR POLLUTION INDEX IN SABAH AND SARAWAK USING PRINCIPAL COMPONENT ANALYSIS AND ARTIFICIAL NEURAL NETWORK
DOI:
https://doi.org/10.24191/VoA.v17i1.11500Keywords:
Air Pollution Index (API), Principle Component Analysis (PCA), Artificial Neural Network (ANN), Varimax rotationAbstract
This study focuses on the identification of Sabah and Sarawak air quality trends based on the data derived from the Department of Environment (DOE). Five Malaysia’s monitoring stations in Sabah and Sarawak were selected based on five air pollutants for four years (2015-2018). This study aims to classify the indicators of variable predictors using the Principal Component Analysis (PCA) method and to compare the best model to predict Air Pollution Index (API) in Sabah and Sarawak using the Artificial Neural Network (ANN) model. After running the varimax rotation, only two pollutants (PM10 and NO2) are the most significant pollutants out of the five pollutants. These two pollutants were used as input layers in Model B and the five pollutants were used as input layers in Model A. These two models were used to compare the best model in the ANN method. The output of ANN models was evaluated through the coefficient of determination (R2) and Root Mean Square Error (RMSE). To identify the best model, the highest value of R2 and the smallest value of RMSE were declared. The findings indicate that the ANN technique has been successfully implemented as a decision-making tool as well as in solving problems for proper management of the atmosphere.
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