Development of a Heterogeneity Analysis Framework for Collaborative Sponge City Management

Abstract

Rapid urbanization, inappropriate urban planning and the changing climate in many countries have resulted in flooding, water shortage and water pollution around the world. Although the sponge city concept has been applied in both macro-scales and micro-scales to address those challenges, research on the heterogeneity of different cities for sponge city construction and the collaborative management between cities is insufficient. Therefore, this paper proposes a multivariate cluster analysis framework and conducts an empirical study using 96 Chinese cities. By considering the local infrastructure, economic development, water resource distribution, water quality and precipitation characteristics in each city, and integrating the principal component analysis and a self-organizing feature mapping network, this paper shows the potential of regional and interregional sponge city collaborative management. This will provide an opportunity for developing a new sponge city management mechanism and will promote the establishment of multi-functional departments for urban flood control and water quality improvement.

Addressing Challenges of Urban Water Management in Chinese Sponge Cities via Nature-Based Solutions

“…The first turning point in China was in 2002, when a societal water-saving construction policy was set by central government [46]. There was further progress between 2003 and 2007, as megacities including Beijing began to better manage stormwater by improving their urban drainage systems and water pollution treatment works [47]. Between 2008 and 2010, water resource-optimization strategies were invoked to optimize the spatial redistribution of water resources [48].…”

“…The promotion and operation of the SCP consider urban water management issues from economic, social, and environmental perspectives [47], and NBS practices suggest that further lessons could be learned regarding adaptation as an aid to planning sustainably in an uncertain future. For example, the Portofino Natural Regional Park, Liguria, Italy, implemented the "RECONECT" project to manage natural hazards in mountainous areas.…”

Application of mechanistic modelling and machine learning for cream cheese fermentation pH prediction

“…However, a traditional neural network builds solely a ‘black box’ system, which does not demonstrate the true relationship between inputs and outputs. Therefore, the trained network has limited application, and often poor extrapolation due to the poor generalisation 22 . What is more, the CPU calculation speed might also be problematic if there is a large amount of input data.…”

“…Therefore, the trained network has limited application, and often poor extrapolation due to the poor generalisation. 22 What is more, the CPU calculation speed might also be problematic if there is a large amount of input data. A recurrent neural network can store the information of previous inputs and combine them with current inputs to represent the relationship between the inputs.…”

Interdisciplinary Inquiry and Spatial Green Stormwater Infrastructure Research

“…In these studies, authors selected criteria perceived as influential to the local natural and built environment, then assigned weights to each criterion. Among the studies included in our review, B. Li et al, 2019 [80], was one of the most representative of this type. In their paper, the authors developed an MCDA model called Environmental Risk Assessment (ERA) to specifically assess land use and identify parcels that are suitable for sustainable land use planning, which they viewed as an essential part of building a healthy and resilient urban ecosystem.…”