Artificial neural network modeling of the water quality index using land use areas as predictors

Water Environment Research : a Research Publication of the Water Environment Federation
Nabeel M GazzazAhmad Zaharin Aris

Abstract

This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.

Citations

Mar 23, 2018·Environmental Monitoring and Assessment·Azadeh AhmadiSara Nazari
Dec 1, 2017·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Lingyun GaoChangrong Wu
Sep 24, 2020·Analytical Methods : Advancing Methods and Applications·Madeleine F DupontKay Latham
Jan 18, 2021·The Science of the Total Environment·Maryam ImaniZoran Kapelan
May 28, 2021·Environmental Science and Pollution Research International·Siddhant Dash, Ajay S Kalamdhad

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