Affiliations 

  • 1 East Coast Environmental Research Institute Universiti Sultan Zainal Abidin Gong Badak, 21300 Terengganu, Malaysia; Department of Forestry Technology, Audu Bako College of Agriculture Dambatta, P.M.B 3159 Kano State, Nigeria. Electronic address: aminu479@gmail.com
  • 2 East Coast Environmental Research Institute Universiti Sultan Zainal Abidin Gong Badak, 21300 Terengganu, Malaysia
  • 3 Department of Forestry Technology, Audu Bako College of Agriculture Dambatta, P.M.B 3159 Kano State, Nigeria
  • 4 Department of Remedial and General Studies, Audu Bako College of Agriculture Dambatta, P.M.B 3159 Kano State, Nigeria
Mar Pollut Bull, 2023 Feb;187:114493.
PMID: 36566515 DOI: 10.1016/j.marpolbul.2022.114493

Abstract

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.