| Title |
Medium-Term Water Consumption Forecasting using Artificial Neural Networks for a Water Utility |
|
Authors |
Velasco, Lemuel Clark; Granados, Angelie Rose; Ortega, Jilly Mae; Pagtalunan, Kyla Veronica |
|
Publication date |
2017 |
|
Conference |
17th Conference of the Science Council of Asia |
| Pages |
6 |
| Publisher |
National Research Council of the Philippines |
| Abstract |
Water is considered to be a valuable universal resource. With an appropriate forecasting model that can generate close to accurate prediction of a locality's water consumption, water utilities can come up with good medium terms plans and measures of water management. This study attempted to develop a forecasting model that will predict the monthly water consumption of a highly urbanized city in five different categories: domestic, commercial, industrial, bulk and whole. An artificial neural network (ANN) was used to analyze the sixteen-year data from the decision making of the city's water utility. Data preparation, model simulation using Neuroph Studio and testing of forecasted results was conducted. The MAPE values of the forecasted and actual results obtained in this study for training and testing was below the stated error range except for Bulk and Industrial. Overall, the model produced results that were within the acceptable error. The results obtained in this research suggest that ANN model is a viable forecasting technique in predicting next month water consumption. |
| URL |
|
Mindanao State
University - Iligan Institute of Technology