Many models or algorithms have been suggested for sensor placement in the drinking water distribution networks, such as genetic algorithms, multiobjective optimization models, and heuristic methods. Because these models or algorithms have high computational demands, however, the requirement of expensive technical computing software is unavoidable. This study presents a rule-based decision support system (RBDSS) to analyze and generate a set of sensor placement locations and compares the performance against 10 optimization models based on four indexes. Our findings show that the RBDSS demands relatively lower computational time and still exhibits outstanding performance in terms of all our indexes when dealing with a large-scale complex drinking water network. (C) 2012 Elsevier Ltd. All rights reserved.