An approach to improve drainage networks based on the study of flood risk

Abstract

[EN] In recent years, the number of pluvial floods in cities has increased. There are different reasons for this increase, but certainly the most important are the increase in the intensity of the rain and the growth of cities, which increases the impervious surface, decreases the concentration time, and consequently increases runoff. To determine the costs of flood damage, land use is very important in the estimation of the cost of flood damage because it defines the type, value, and vulnerability of the structures. On this wise, an approach of great interest to managers of drainage networks is the estimation of annual damages in the area under study, because by knowing these costs, corrective measures can be taken, and the investment needed to reduce flooding can be determined. This work aims to present a methodology to improve drainage networks with lack of capacity due to increased runoff with a focus on the study of the vulnerability to the risk of flooding. To improve the operation of the networks, the method considers the possibility of changing the existing pipes for others with greater capacity, the construction of storm tanks and the implementation of hydraulic controls. To find the best solutions, an optimization model was developed that uses a Pseudo Genetic Algorithm and the SWMM model to perform hydraulic simulations. To reduce calculation times, a search space reduction procedure is applied to identify the regions containing the best solutions. These actions are intended to improve the efficiency of the model. For the economic assessment of flood risks, the cost of flood damage is related to its probability of occurrence for different return periods. With these data, a curve with a log-linear relationship is constructed. The cost of the flood risk is obtained by integrating the area under the curve obtained. The methodology is applied in a drainage network with flooding problems called Balloon, located in located in northern Italy. The results are useful to demonstrate the benefit of risk cost analysis to make decisions and underline the relevance of including optimization models to prevent future damage in cities.