Full length articleIntegrating socioecological indexes in multiobjective intelligent optimization of green-grey coupled infrastructures
Graphical abstract
Introduction
Rapid urbanization has intensively influenced urban runoff hydrological processes and posed challenges to urban stormwater runoff control managers. Maintaining the safety of urban drainage systems and the sustainable ecological system of receiving waters are as important as economic development. Traditional grey infrastructure and rapidly developing green infrastructure are effective methods for stormwater runoff control (Dong et al., 2017; Leng et al., 2020; Li and Davis, 2016). Trade-offs need to be made between multiple objectives and green-grey infrastructure configurations. It is difficult to enumerate vast infrastructure configurations manually due to numerous objectives and variables. Manually set configurations that rely on subjective experience maybe not appropriate for regional conditions. Rapid advances in computer science are revolutionizing urban runoff control management. Hydrological models as an effective tool have been widely used to simulate runoff processes in real-time (Zeng et al., 2021). The performance of the infrastructure can be evaluated based on real-time simulation. Evaluation feedback over a period can help to guide improvements. Thus, a comprehensive evaluation system for green-grey coupled infrastructure is needed to describe the multiobjectives of optimization. An intelligent optimization algorithm is necessary to achieve automatic optimization of runoff control infrastructure scale.
Traditional storm water management relies on grey infrastructures to achieve runoff control goals through rapid drainage and centralized treatment (Arif et al., 2020; Bisinella de Faria et al., 2015). For ecologically sustainable urban development, green infrastructures are proposed to assist grey infrastructures by restoring natural retention and infiltration, thereby reducing runoff coefficients (Li and Davis, 2016; Zhang and Chui, 2019). Green and grey infrastructures are not mutually exclusive. Drainage systems that combine green and grey runoff control infrastructures tend to show better performance than green-only or grey-only infrastructure systems (Dong et al., 2017; Leng et al., 2020; Zeng et al., 2019). In existing studies, due to the lack of a unified quantitively evaluation system for green and grey infrastructure, the respective contribution of green and grey infrastructures in runoff control cannot be quantified, thereby leading to the inability to achieve optimal system performance. Most of the literature on green-grey optimization only take drainage network as grey infrastructure (Alves et al., 2019; Alves et al., 2020; Leng et al., 2020; Xu et al., 2019), without considering the terminal runoff control measures such as pumping station and interception treatment, thus failing to cope with runoff control at the watershed scale (such as combined system overflow pollution control).
In the face of climate and ecological change, sustainable and livable cities are inevitably the new demands of urban managers(Dong et al., 2020). China's goal of “carbon neutral” implies a transition from resource-based development to green and low-carbon development. The life cycle assessment (LCA) method has been proved reliable in evaluating the socioecological impact of runoff control infrastructure (Petit-Boix et al., 2017; Xu et al., 2017). This method considers the life cycle socioecological impact at the stages of material acquisition, construction, operation and maintenance, and disposal. Socioecological paybacks of urban runoff control on a specific aspect, such as flood disaster reduction and energy saving, have been evaluated using LCA method (He et al., 2020; Tighnavard Balasbaneh et al., 2019). For the trade-off between socioecological investments (SEIs) and benefits, the multiple paybacks of runoff control infrastructure should be considered rather than only one specific aspect. A research gap exists that the socioecological influences of infrastructure have not been evaluated in a uniform framework with control functions and capital investments
When faced with trade-offs between multiple runoff control objectives, some manually arranged schemes are often presented for comparison (Baek et al., 2015; Farreny et al., 2011; Xu et al., 2017). However, these schemes cannot achieve optimal system performance and may not meet government management objectives (Leng et al., 2020). When trade-offs are only focused on runoff control functions and capital investments in the system, intelligent optimization algorithms have been proven efficient (Liu et al., 2016; Raei et al., 2019). The 2D optimal objectives of runoff control function and capital investment cannot meet the current emphasis on the sustainable development of ecological system. Therefore, combining socioecological objectives with traditional objectives is challenging to achieve optimal 3D objectives for the determination of runoff control infrastructure scale.
Considering the results and limitations of existing research, nondominated sorting genetic algorithm-II (NSGA-II) was utilized for the optimization of multiobjectives. Scenarios with different green and grey infrastructure scales were considered as individuals in the optimization algorithm. The performances of scenarios were comprehensively evaluated in terms of runoff control function (RCF), capital investment (CI), and socioecological return on investment (SEROI). The 3D objectives were quantified through hydrological model simulation, life cycle cost (LCC) analysis, and LCA method. This multiobjective intelligent optimization was applied to a sponge city pilot region in Wuhan, China, as a case study. Some tradeoffs were conducted in the Pareto optimal runoff control scenarios. The contributions of green and grey infrastructures to the three optimization objectives were explored.
Section snippets
Methods and data
An intelligent optimization algorithm based on a multiobjective evaluation framework was applied to a green-grey coupled infrastructure runoff control system in this study. This process was performed to address the problem where manually arranged schemes cannot perform optimally in the multiobjective and multi-infrastructure stormwater runoff management. The methodological framework includes five main parts, as shown in Fig. 1. (I) Build hydrological models based on the characteristics of the
Identification of the dormitory stages of life cycle CI
The dormitory CI stages of individual infrastructures during their lifetime were explored to a large extent depending on their functions. When the CI of infrastructures was normalized to their functional unit and lifespan, the initial one-time investment was diluted within each year, and a remarkable investment share was taken by the annual operational stages of some infrastructures. Life cycle capital investment inventory was shown in Appendix D, Table D.1.As shown in Fig. 5, the major
Conclusions
The NSGA-II intelligent optimization algorithm was utilized for the optimization of multiobjectives in this study to achieve the reasonable scale of various runoff control infrastructures. A multiobjective hierarchical evaluation system was established for the green-grey coupled runoff control infrastructure system. Specifically, we obtained the following important progress.
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The quantification methods for indexes in multiobjective evaluation system were constructed to evaluate green-grey coupled
CRediT authorship contribution statement
Zijing Liu: . Changqing Xu: . Te Xu: . Haifeng Jia: . Xiang Zhang: . Zhengxia Chen: Writing – review & editing. Dingkun Yin: Writing – review & editing.
Declaration of Competing Interest
We confirm that there are no known conflicts of interest associated with this publication. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author
Acknowledgement
This work was supported by the National Nature Science Foundation of China (Grant No. 41890823, 52070112, 7181101209, 51761125013). The research is also supported by Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment (Suzhou 215009, China).
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