Multi-objective layout optimization for sponge city by annealing algorithm and its environmental benefits analysis

Low impact development (LID) practices are able to mitigate the detrimental effects of urbanization and climate change due to their salient design features. LID can restore the hydrology of urban areas to the pre-development functions by using distributed stormwater control and natural hydrological features. LID can help to achieve the goal of sustainable development as it promotes effective urban stormwater management. This review covers a comprehensive list of LID practices, namely bioretention cell, green roof, infiltration trench, permeable pavement, rain barrel or cistern, rooftop disconnection and vegetative swale. For each type of the LID, the recent advances covering the aspects of principles, design, performance, advantages and disadvantages and costs are systematically reviewed. Additionally, although LID has been quite broadly applied and demonstrated success in urban stormwater management in many countries, there are still some main challenges during the implementation such as clogging and water quality. Meanwhile, this review also highlights the great opportunities for further developments for LID practices to realize their wider practical application. Finally, future research directions are provided in order to give critical insights into potential future works to advance this field of research.

Low Impact Development (LID) Practices: A Review on Recent Developments, Challenges and Prospects

Spatial layout optimization of green infrastructure based on life-cycle multi-objective optimization algorithm and SWMM model

Green-grey infrastructures implementation for addressing urban flood damages and urban drainage system’ (UDS) resilience (respectively) needs comprehensive evaluation under various planning and design influencing factors. In this paper, a multi-objective decision-making framework is developed for the optimization of green-grey infrastructures to understand the trade-off between benefits and retrofit costs under various design configurations, rainfall intensities, and system performance indicators. In the developed framework, the grey infrastructure locations are optimally determined by using Analytic Hierarchy Process (AHP) method with three defined indicators (e.g., flood volume and flood duration of flooding nodes, sensitivity of flooding nodes) and maximum percentage of each type of green infrastructure (GI) in each sub-catchment based on land-use analysis as a preliminary step of the multi-objective optimization process, followed by decision-making procedure. The application results reveal that optimal designs of grey infrastructure are more cost-effective and may have a significant influence on the reduction of flood damages and outlet peak flow compared to GI from their individual evaluations, while GI improves UDS’ functionality performance indicators (e.g., resilience, reliability, and sustainability) better than grey infrastructure. However, a combined implementation of both infrastructures boosts their advantages because of the synchronization effect compared to individual scenarios, providing the most cost-effective optimal UDS designs. Moreover, the cost-effectiveness of coupled green-grey infrastructures designs could be enhanced from lower to higher design storms because of higher rate of utilization of stormwater infiltration and storage capacities of both infrastructures. The framework presented in this study can be conveniently generalized to other case-studies for optimal implementation of green-grey infrastructures as long as including their local economic parameters, climatic patterns, urban development plans and green-grey infrastructures design guidelines. 

A multi-objective decision-making framework for implementing green-grey infrastructures to enhance urban drainage system resilience

Urban waterlogging often causes urban disasters, and the rapid early warning and comprehensive analysis of the urban waterlogging can help disaster defenses. However, the warning of waterlogging through the monitoring data cannot give grid distribution and the forecast of hydrological models cannot ensure rapid early warning. To obtain a grid rapid early warning result for a region, like an urban area, a method needs to be proposed which can meet the above problems. In this research, AutoML (automatic machine learning based on genetic algorithm) was recommended to construct the rapid early warning and comprehensive analysis models for urban waterlogging by compared with the other three machine learning algorithms, CatBoost (Categorical Boosting), XGBoost (eXtreme Gradient Boosting), and BPDNN (Back Propagation Deep Learning Neural Network). In the models, the forecast and historical precipitation obtained from the Integrated Nowcasting through Comprehensive analysis system (INCA), the difference of elevation, and the urban waterlogging risk maps provided by Tianjin Meteorological Administration were employed as the input sources. The input precipitation duration was determined as 12 h based on the sensitivity analysis of the influence of various precipitation duration on waterlogging depths. Due to the non-digital (discrete dataset) features, the urban waterlogging risk maps were transformed to the weight of each corresponding risk level according to the area of each risk level and the number of samples falling in each risk level. The difference of elevation was characterized by the average elevations of various distances from the points of concern. The output waterlogging depths were compared with the waterlogging depths monitored in Tianjin, China, whose quality was controlled by eliminating the records of the waterlogging depths lasting for a long time after the end of rainfall. The comparison of the models constructed by different methods demonstrated that the AutoML performed better (NSE and R2 > 0.92, CC > 0.95, RMSE1.1–1.9 cm) than the other three models. The forecast waterlogging depths by AutoML was also coherent with the monitoring waterlogging depths (NSE and R2 ≥ 0.9, CC ≥ 0.95, RMSE 1.7–2.2 cm). For that local topography and waterlogging risk are considered, the AutoML models can be used in the area without the monitoring of water level, quickly predict waterlogging depths and give spatial grid results for rapidly early warning. 

Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms

Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks.

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Multi-objective layout optimization for sponge city by annealing algorithm and its environmental benefits analysis

Establishment of urban waterlogging pre-warning system based on coupling RBF-NARX neural networks.