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Life cycle thinking and machine learning for urban metabolism assessment and prediction
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Orientador(es)
Resumo(s)
The real-world urban systems represent nonlinear, dynamical, and interconnected urban processes that require
better management of their complexity. Thereby, we need to understand, measure, and assess the structure and
functioning of the urban processes. We propose an innovative and novel evidence-based methodology to manage
the complexity of urban processes, that can enhance their resilience as part of the concept of smart and
regenerative urban metabolism with the overarching intention to better achieve sustainability. We couple Life
Cycle Thinking and Machine Learning to measure and assess the metabolic processes of the urban core of Lisbon’s functional urban area using multidimensional indicators and measures incorporating urban ecosystem
services dynamics. We built and trained a multilayer perceptron (MLP) network to identify the metabolic drivers
and predict the metabolic changes for the near future (2025). The prediction model’s performance was validated
using the standard deviations of the prediction errors of the data subsets and the network’s training graph. The
simulated results show that the urban processes related to employment and unemployment rates (17%), energy
systems (10%), sewage and waste management/treatment/recycling, demography & migration, hard/soft cultural assets, and air pollution (7%), education and training, welfare, cultural participation, and habitatecosystems (5%), urban safety, water systems, economy, housing quality, urban void, urban fabric, and health
services and infrastructure (2%), consists the salient drivers for the urban metabolic changes. The proposed
research framework acts as a knowledge-based tool to support effective urban metabolism policies ensuring
sustainable and resilient urban development.
Descrição
Palavras-chave
Life cycle inventory Sensitivity analysis ANN Urban core Case study Land use planning Urban metabolism
Contexto Educativo
Citação
Peponi, A., Morgado, P., & Kumble, P. (2022). Life cycle thinking and machine learning for urban metabolism assessment and prediction. Sustainable Cities and Society, 80, 103754. https://doi.org/10.1016/j.scs.2022.103754
Editora
Elsevier