---

A novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.

---

Compared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost

solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points

of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic

disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase

approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized

in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the

location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate

levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified

number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the

second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to

minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels

of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of

chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective

genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different

numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can

explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the tradeoff

between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels.