Life cycle thinking and machine learning for urban metabolism assessment and prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F22%3A94137" target="_blank" >RIV/60460709:41330/22:94137 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2073-4441/14/13/2005" target="_blank" >https://www.mdpi.com/2073-4441/14/13/2005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.scs.2022.103754" target="_blank" >10.1016/j.scs.2022.103754</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Life cycle thinking and machine learning for urban metabolism assessment and prediction

Popis výsledku v původním jazyce

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 Lisbons 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 models performance was validated using the standard deviations of the predi

Název v anglickém jazyce

Life cycle thinking and machine learning for urban metabolism assessment and prediction

Popis výsledku anglicky

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 Lisbons 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 models performance was validated using the standard deviations of the predi

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sustainable Cities and Society

ISSN

2210-6707

e-ISSN

2210-6715

Svazek periodika

2022

Číslo periodika v rámci svazku

80

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

000780381200002

EID výsledku v databázi Scopus

2-s2.0-85124386598