Urban Drainage Systems (UDS) are one of the most vital yet, complex infrastructures that support people's livelihood in urban areas. However, due to their mainly underground infrastructure and complexity, the planning and management of UDS are usually associated with high investment, which stakeholders sometimes overlook. As long-lived infrastructures, UDS’s limited capability is being put under constantly increasing pressures. Amongst the pressures, the global effects of climate change on rainfall extremes is the most important. As climate change affects the rainfall extremes and the overall hourly and daily rainfall events, urban flooding issues are becoming more costly to manage. Several rehabilitation efforts have been made to address this issue with minimum cost and optimal performance in flood reduction by increasing the resiliency of UDS in order to minimise the duration and magnitude of urban flooding.

Rehabilitation of UDS can be done in several ways, including implementing Green-Blue-Grey Infrastructures (G-B-G measures). The combination of G-B-G measures can increase the resiliency of the UDS to withstand higher intensity rainfall by reducing both the peak flow and enlarging the capacity of the UDS system. Therefore, this thesis aims to develop a method to find the optimal way to rehabilitate an existing UDS to reduce the risk of flooding under the climate change rainfall scenarios.

The method developed coupled a hydrodynamic model, Storm Water Management Model (SWMM), and Genetic Algorithm (GA) to find the optimal solution to rehabilitate UDS. The effect of climate change was incorporated by simulating the solutions using composite design storms that represent the increase in hourly and daily rainfall extremes for 2030, 2050, and 2085. The objective function of this optimisation problem becomes the minimisation of the total cost to implement the measures for the rehabilitation of UDS, under the constraint that no flooding can happen on the system when tested against the climate change rainfall scenarios. Therefore, the decision variables of this optimisation are the size and location for each implemented measure, while the penalty cost is associated with the cost of each m3 of flooding.

Based on the analysis of the case study, the most appropriate Green-Blue measures to be implemented is Rain Barrels, Infiltration Trenches, and Pervious Pavements. Meanwhile, for grey measures, it is best to consider pipe and pump replacements and increasing the CSOs’ weirs. The optimisation was done using the developed formal method and manual trial-and-error. The results of the formal optimisation have been confirmed to outperform the result from manual optimisation using the traditional trial-and-error method. The optimal solutions proved that a combination of both grey and G-B measures produced the lowest cost to reduce flooding. Although the solutions can be adapted over time from 2030 until 2085, the results show that adaptive solutions might not be needed when the solution for 2085 is better implemented from the year 2030. Overall, it can be projected that in the future, the combination of G-B-G measures can produce an economically optimal solution to be implemented in order to achieve zero floodings in the case study location.