Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty assoc...

The short-term, demand-forecasting model described in this paper forms the third constituent part of the POWADIMA research project which, taken together, address the issue of real-time, near-optimal control of water-distribution networks. Since the intention is to treat water distribution as a feed-forward control system, operational decisions have to be based on the expected future demands for water, rather than just the present known requirements. Accordingly, it was necessary to develop a short-term, demand-forecasting procedure. To that end, monitoring facilities were installed to measure short-term fluctuations in demands for a small experimental network, which enabled a thorough investigation of trends and periodicities that can usually be found in this type of time-series. On the basis of these data, a short-term, demand-forecasting model was formulated. The model reproduces the periodic patterns observed at annual, weekly and daily levels prior to fine-tuning the estimated values of future demands through the inclusion of persistence effects. Having validated the model, the demand forecasts were subjected to an analysis of the sensitivity to possible errors in the various components of the model. Its application to much larger case studies is described in the following two papers.

In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy). It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.