A novel framework of multivariate modeling of water distribution network through 3³ factorial design and artificial neural network

Abstract

The water distribution network is largely affected by the change in the influencing factors, such as input pressure, demand and supply duration. The change in each parameter requires the extensive design of the network and the interactive effect of the influencing parameters are hardly explored. The main hurdles for the water providers lie in the absence of a prediction model, which can be used as a decision tool to assess the effect of the change in parameter and estimating the cost for the changed scenario. The present study developed a novel framework based on the artificial neural network for multivariate prediction modeling taking the response as the cost of the pipe network. The application of the 3³ factorial design was used for the selection of the influencing parameters and outcome was taken as the input to the neural network model. The adequacy of the model was tested through error functions and analysis of variance. The low values of the error functions (0.0004–0.228) and high F value (162,442) and R² (0.999) established the significance of the model. The model can be used for predicting the cost of the changed scenarios and assessment of the optimal solution for the system variables.

Keywords:

• Water distribution networks
• 3³ factorial design
• artificial neural network
• hydraulic design

Disclosure statement

No potential conflict of interest was reported by the authors.