Decision making in flood risk based storm sewer network design.

It is widely recognised that flood risk needs to be taken into account when designing a storm sewer network. Flood risk is generally a combination of flood consequences and flood probabilities. This paper aims to explore the decision making in flood risk based storm sewer network design. A multiobjective optimization is proposed to find the Pareto front of optimal designs in terms of low construction cost and low flood risk. The decision making process then follows this multi-objective optimization to select a best design from the Pareto front. The traditional way of designing a storm sewer system based on a predefined design storm is used as one of the decision making criteria. Additionally, three commonly used risk based criteria, i.e., the expected flood risk based criterion, the Hurwicz criterion and the stochastic dominance based criterion, are investigated and applied in this paper. Different decisions are made according to different criteria as a result of different concerns represented by the criteria. The proposed procedure is applied to a simple storm sewer network design to demonstrate its effectiveness and the different criteria are compared.

Use of surrogate modelling for multiobjective optimisation of urban wastewater systems.

Simulation models are now available to represent the sewer network, wastewater treatment plant and receiving water as an integrated system. These models can be combined with optimisation methods to improve overall system performance through optimal control. Evolutionary algorithms (EAs) have been proven to be a powerful method in developing optimal control strategies; however, the intensive computational requirement of these methods imposes a limit on their application. This paper explores the potential of surrogate modelling in multiobjective optimisation of urban wastewater systems with a limited number of model simulations. A surrogate based method, ParEGO, is combined with an integrated urban wastewater model to solve real time control problems. This method is compared with the popular NSGA II, by using performance indicators: the hypervolume indicator, additive binary epsilon-indicator and attainment surface. Comparative results show that ParEGO is an efficient and effective method in deriving optimal control strategies for multiple objective control problems with a small number of simulations. It is suggested that ParEGO can greatly improve computational efficiency in the multiobjective optimisation process, particularly for complex urban wastewater systems.

From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms.

The calibration of storm water runoff models is a complex task. Early attempts focused on the choice of a performance criterion function that could capture all the facets of the problem into a single-objective framework. Subsequently, the awareness that a good calibration must necessarily take into account conflicting objectives led to the adoption of more sophisticated multi-objective approaches. Only recently, the focus has shifted towards effective ways of exploiting the mounting information provided by the availability of many sets of concurrent rainfall and flow measurements. This paper revisits through a case study the transition just elucidated: the calibration of a SWMM model applied to a catchment in Singapore is tackled through a single-objective, a multi-objective and a multi-objective multiple-event (MOME) paradigm respectively. A new approach to support the latter is presented herein. It consists in formulating the problem of model calibration as a multi-objective problem with m x r objective functions, where m and r are the number of performance criteria and rainfall events respectively, that must be optimized simultaneously. Results suggest that the new MOME framework performs significantly better than the others tested on the case study presented.

Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.