Search

Stormwater green infrastructure (GI) has been applied as a method to address urban flooding for over forty years. However, GI has not yet been widely utilized across the US. Prior studies identified some challenges with technical, engineering, and socioeconomic aspects of GI, but detailed examination of non-scientific papers to identify and analyze real-world barriers that may hinder the implementation of GI has not yet been done. To achieve this goal, we conducted a meta-analysis of 351 public documents from federal, state, and 62 municipalities in Eastern Pennsylvania to systematically review the support of different levels of government for implementing GI. We summarized barriers in three categories: 1) governance and policy: failure to integrate GI into existing stormwater management policies due to unclearly defined responsibility and infrequent policy updates, 2) stormwater fees and credits: inequity in stormwater fee structures and debate of stormwater credits connected to GI, and 3) public education and outreach: most municipal governments provide little or no stormwater and GI-related information to their residents, but some municipalities with higher population density have tried to offer more. These barriers will restrain the original intention and vision of GI implementation and cause difficulties in effectively conveying GI's information from the federal and state levels to municipalities and residents. Our discussion highlights these difficulties of GI implementation at all governmental levels and shed light on potential solutions to address these barriers. • Barriers to Green Infrastructure (GI) adoption in the US context are identified. • Qualitative and quantitative methods are conducted to identify GI barriers. • Three categories of GI barriers are summarized at different levels of government. [ABSTRACT FROM AUTHOR]

The main challenge in addressing constrained multi-objective optimization problems (CMOPs) lies in achieving a balance among convergence, diversity, and feasibility. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm with Pareto estimation via neural network named CMOEA-PeNN. In order to exploit and explore the decision space, the proposed algorithm employs a dual-population mechanism, which is trained with a self-organizing map (SOM). Firstly, the population distribution structure in decision space is mapped to objective space while preserving neighborhood information, and then the neuron weight is utilized to estimate the Pareto front (PF). Secondly, a novel approach is devised to preserve the feasibility of the population and enhance the estimation of the Pareto front by SOM. The achievement scalarizing function (ASF) is employed to choose promising solutions. This strategy could guide the population toward the optimal solution while exploring the small feasible regions. Finally, the performance of CMOEA-PeNN is compared with five state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) on three widely used benchmark problems and a real-world problem. The experimental results show that CMOEA-PeNN could archive competitive performance in solving CMOPs. [ABSTRACT FROM AUTHOR]