A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems
Different studies have been carried out to evaluate the progress made by countries and cities towards achieving sustainability to compare its evolution. However, the micro-territorial level, which encompasses a community perspective, has not been examined through a comprehensive forecasting method of sustainability categories with machine learning tools. This study aims to establish a method to forecast the sustainability levels of an urban ecosystem through supervised modeling. To this end, it was necessary to establish a set of indicators that characterize the dimensions of sustainable development, consistent with the Sustainable Development Goals. Using the data normalization technique to process the information and combining it in different dimensions made it possible to identify the sustainability level of the urban zone for each year from 2009 to 2017. The resulting information was the basis for the supervised classification. It was found that the sustainability level in the micro-territory has been improving from a low level in 2009, which increased to a medium level in the subsequent years. Forecasts of the sustainability levels of the zone were possible by using decision trees, neural networks, and support vector machines, in which 70% of the data were used to train the machine learning tools, with the remaining 30% used for validation. According to the performance metrics, decision trees outperformed the other two tools.
Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems
Different studies have been carried out to evaluate the progress made by countries and cities towards achieving sustainability to compare its evolution. However, the micro-territorial level, which encompasses a community perspective, has not been examined through a comprehensive forecasting method of sustainability categories with machine learning tools. This study aims to establish a method to forecast the sustainability levels of an urban ecosystem through supervised modeling. To this end, it was necessary to establish a set of indicators that characterize the dimensions of sustainable development, consistent with the Sustainable Development Goals. Using the data normalization technique to process the information and combining it in different dimensions made it possible to identify the sustainability level of the urban zone for each year from 2009 to 2017. The resulting information was the basis for the supervised classification. It was found that the sustainability level in the micro-territory has been improving from a low level in 2009, which increased to a medium level in the subsequent years. Forecasts of the sustainability levels of the zone were possible by using decision trees, neural networks, and support vector machines, in which 70% of the data were used to train the machine learning tools, with the remaining 30% used for validation. According to the performance metrics, decision trees outperformed the other two tools.
Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems
Nidia Isabel Molina-Gómez (author) / Karen Rodríguez-Rojas (author) / Dayam Calderón-Rivera (author) / José Luis Díaz-Arévalo (author) / P. Amparo López-Jiménez (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs
| DOAJ | 2021
Computational System to Classify Cyber Crime Offenses using Machine Learning
| DOAJ | 2020
Sustainability in Urban Ecosystems: Beyond an Object of Study
| British Library Conference Proceedings | 1994
Practical Evaluation Tools for Urban Sustainability
| Online Contents | 2007
Practical Evaluation Tools for Urban Sustainability
| British Library Conference Proceedings | 2007