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Hybridization of Artificial Neural Network (ANN) with Multiple Linear
Regression (MLR) and Principal Component Regression (PCR) for Prediction
of Chlorophyll-a Water Quality in Sri Manjung Perak
Nor Azaliah Binti Rozali
Supervisor: Assoc. Prof. Dr. Muhamad Safiih Bin Lola
Bachelor of Science (Computational Mathematics)
School of Informatics and Applied Mathematics
Chlorophylls represent in our daily life with their natural colour of earth, green. It is
greenish pigments that found in plant to absorb energy. Objectives of this research are
to develop new model artificial neural network (ANN) with multiple linear regression
(MLR) and principal component regression (PCR) method for prediction of chlorophyll-a
water quality and to test the effectiveness of the model developed in by using
hybridization ANN with MLR and PCR. Issues highlighted here are to develop the best and
most accurate method to predict the chlorophyll-a in water quality and to determine the
comparison between hybridization of ANN with MLR and PCR methods. The results of this
comparison methods show that only one method is chosen to develop the new model of
ANN. It can be concluded, once the model developed from the method chosen, the ANN
model able to gain the new data set.
966 | UMT UNDERGRADUATE RESEARCH DAY 2018