Abstract:
As a measure of water quality, water turbidity might be a source of water pol lution in drinking water resources. Henceforth, having a reliable tool for predicting
turbidity values based on common water quantity/quality measured parameters
is of great importance. In the present paper, the performance of the online se quential extreme learning machine (OS-ELM) in predicting daily values of turbidity
in Brandywine Creek, Pennsylvania, is evaluated. For this purpose, in addition to
the developed OS-ELM, several data-driven models, that is, multilayer perceptron
neural network (MLPANN), the classification and regression tree (CART), the group
method of data handling (GMDH) and the response surface method (RSM) have
been applied. The general findings of the study confirm the superiority of the
OS-ELM model over the other applied models so that the OS-ELM improved the
averaged RMSE of the predicted values 9.1, 11.7, 20.5 and 29.3% over the ML PANN, GMDH, RSM and CART models, respectively.
