Single input single output time series artificial neural network models for free residual chlorine forecasting in water distribution networks
Soyupak, S.; Kilic, H.; Karadirek, I.E.; Muhammetoglu, H.
The aim of this study is to investigate the utilization of Single Input Single Output Time Series Artificial Neural Networks models as a forecasting tool for estimating Free Residual Chlorine levels at critical locations of fairly complex Water Distribution Systems. The response surface methodology was adopted in identifying performance and precision trends as a function of number of steps used as inputs and number of steps ahead to predict (Horizons). The utilized response surfaces were for coeefficient of determination and mean absolute error. The creation of response surfaces was achieved by developing Artificial Neural Network models for several combinations of number of steps used as inputs and number of steps ahead to predict that enable the calculations of coefficient of determination and mean absolute error for the selected combinations. Then these results have been assembled to obtain contour maps by distance weighted least square technique. The maximum attained coefficient of determination levels were within the range 0.656 to 0.974, while minimum achievable mean absolute error levels were within the range 0.0080 to 0.0284 ppm. The achieved mean absolute error is very low when compared with the followings: a) the applied Free Residual Chlorine levels from the source which is about 0.5 ppm and b) the minimum detection limit of the chlorine analyzers given as 0.01 ppm.... Show more Show less