Article ID: | iaor200971465 |
Country: | Netherlands |
Volume: | 23 |
Issue: | 9 |
Start Page Number: | 1725 |
End Page Number: | 1742 |
Publication Date: | Jul 2009 |
Journal: | Water Resources Management |
Authors: | Lee Jung Ho, Baek Chun Woo, Kim Joong Hoon, Jun Hwan Don, Jo Deok Jun |
Keywords: | artificial intelligence: decision support |
The main objective of sewer rehabilitation is to improve its function while eliminating inflow/infiltration (I/I). If we can identify the amount of I/I for an individual pipe, it is possible to find the distribution of the total I/I over the entire sewer system. With this information we identify which sub-area is more critical than others. However, in real, the amount of I/I for an individual pipe is almost impossible to be obtained due to the limitation of cost and time. For this reason, we suggested the rehabilitation weighting model (RWM) to determine it objectively or systematically. Based on the determined amount of I/I for an individual pipe, we also suggested the rehabilitation priority model (RPM), which is equipped with genetic algorithm, to determine the optimal rehabilitation priority (ORP) for sub-areas in term of minimizing the amount of I/I occurring while the rehabilitation process is performed. The benefit obtained by implementing the ORP for rehabilitation of sub-areas is estimated by the only waste water treatment cost (WWTC) of I/I which occurs during the sewer rehabilitation period. A decision making support system which is consisted of the RWM and the RPM was applied to an urban drainage area. The results of the ORP were compared with those of a numerical weighting method (NWM) and the worst order which are other methods to determine the rehabilitation order of sub-areas in field. The ORP reduced the WWTC by 22% compared to the NWM and by 40% compared to the worst order.