Numerical Study of Back Propagation Learning Algorithms for Forecasting Water Quality Index

Artificial intelligence techniques, such as neural networks are modeling tools that can be applied to predict water quality parameters. Artificial neural networks are frequently used to model various highly variable and nonlinear physical phenomena in the water and environmental engineering fields. This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI) for Batlagundu, Dindigul District, Tamilnadu. The modeling efforts showed that the optimal network architecture was 8-3-1 and that the best WQI predictions were associated with the back propagation (BP) algorithm. The WQI predictions of this model had significant, positive, very high correlation with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value or range of values of the constituent water quality variables.