Diagnosing Error Prone Application of Optimal Model Calibration

Optimization tools have been developed for automatic calibration of water distribution models. The tools are often based upon a powerful genetic algorithm optimization and seamlessly integrated with hydraulic and water quality modeling systems. It provides the advanced features for engineers and modelers. In spite of the power of the GA modeling, some users report that the optimal calibration tool has not worked effectively for practical model calibration. This paper presents a case study on uncovering the causes of problems in automated calibration and proposes an approach for effectively applying the optimization calibration method. The case study involves a real water distribution system. The model is constructed by experienced modeling engineers and an optimization calibration tool is applied to calibrating the model. However, the initial application of the calibration tool did not make any improvement at all over the uncalibrated model. A detailed analysis has been conducted to diagnose the problems with the model calibration. This study uncovers an error prone application of the optimization-based calibration tool and illustrates effective procedures for applying the calibration tool to a real water distribution model. The procedure and steps have been found efficient at improving model calibration. They may also serve the general guidelines for calibrating water distribution models even without use of optimization.