A Systematic Exploration of Uncertainty and Convergence of Inverse Transient Calibration for WDSs

Despite over ten years of research into ITC techniques for water distribution systems, many problems remain. One reason for these difficulties is that real water distribution systems invariably have many other uncertainties in addition to the leakage rates and friction factors that are conventionally considered as unknowns. For example, properties such as pipe diameter, wave speed, the possible presence of air, the value of the water demand at the time of the tests, and uncertain measurement accuracy, all add to the complexity and difficulty of obtaining a reliable calibration. The current paper investigates quantitatively how several of these uncertainties deteriorate system calibration, and thus the paper generally considers the necessity of a systematic calibration approach to explicitly include these additional uncertainties during the ITC process. To this end, two evolutionary optimizations, namely Genetic Algorithms and Particle Swarm Optimization, are compared and contrasted during the ITC iterations. The advantage of the evolutionary algorithms is that they help the search to escape from poor local optima in multifaceted and complicate problems and thus to locate a good global (or near-global) optimum. However, even these approaches can often be expected to converge poorly when the full scale of the field problem is reflected in the search space.