Integração de algoritmos genéticos ao modelo hidrológico SWMM na otimização de sistemas de drenagem urbana sustentável

Abstract

High rates of soil imperviousness, intensified by urbanization, have been contributing strongly to the occurrence of floods all over the world. In order to mitigate these impacts, Low Impact Development (LID) techniques seek to preserve the hydrology of urban catchments closer to pre-development conditions by using distributed stormwater control systems. Nevertheless, the application of these techniques is associated with a variety of challenges, including the design of the LID controls, due to the great number of variables involved and the need to attend to multiple objectives simultaneously. Within this context, the application of hydrologic simulation models integrated with optimization techniques is being recently explored as an alternative to assist the planning of LID scenarios. This work aims to verify the applicability of an adaptation of the Genetic Algorithm NSGA-II, together with the hydrologic model SWMM, to assist the optimal design of LID scenarios seeking to reduce the stormwater runoff and the implementation and maintenance costs on different return periods. The scenarios have considered the implementation of permeable pavements, green roofs and bioretention cells either individually or integrated. The results showed that the model was capable of finding a great variety of optimal solution on various levels of runoff reduction, at different costs, to all situations considered. Each scenario presented different cost-efficiency and trade-off trends for the optimal solutions and the objective functions, respectively, according to the limitations and potentials of the correspondent LID and with the complexity of the scenario. Regarding the applicability of the optimization model as a LID design method, it was observed that various optimal solutions presented an oversizing of the storage and soil layer. Besides that, it was possible to notice that the model also presented difficulties to consider null areas for all scenarios. Therefore, suggestions on how to improve the model have been made to solve the identified problems. At last, considering the adopted project parameters, the inclusion or exclusion of the maintenance costs in the optimization process did not alter significantly the general configuration of the optimal solutions. However, the importance of these costs cannot be underestimated otherwise it can lead to quite serious economic damages.