APPLICATION OF ARTIFICIAL NEURAL NETWORK MODELS FOR PREDICTING WATER QUALITY INDEX
DOI:
https://doi.org/10.11113/mjce.v16.15664Keywords:
Artificial Neural Network, Multiple Linear Regression, Water Quality Index (WQI).Abstract
This study discusses the development and validation of an Artificial Neural
Network (ANN) model in estimating water quality index (WQI) in the Langat River
Basin, Malaysia. The ANN model has been developed and tested using data from 30
monitoring stations. The modeling data was divided into two sets. For the first set, ANNs
were trained, tested and validated using six independent water quality variables as input
parameters. Consequently, Multiple Linear Regression (MLR) was applied to eliminate
independent variables that exhibit the lowest contribution in variance. Independent
variables that accounted for approximately 71% of the variance in WQI are Dissolved
Oxygen (DO), Biochemical Oxygen Demand (BOD), Suspended Solids (SS) and
Ammoniacal-Nitrate (AN). The Chemical Oxygen Demand (COD) and pH contributed
only 8% and 2% to the variance, respectively. Thus, in the second data set, only four
independent variables were used to train, test and validate the ANNs. We found that the
correlation coefficient given by six independent variables (0.92) is only slightly better in
estimating WQI compared to four independent variables (0.91) which demonstrates that
ANN is capable of estimating WQI with acceptable accuracy when it is trained by
eliminating COD and pH as independent variables.
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