Using Machine Learning Tools to Classify Sustainability Levels in the Development of Urban Ecosystems

Nidia Isabel Molina-Gómez, Karen Rodríguez-Rojas, Dayam Calderón-Rivera, José Luis Díaz-Arévalo and P. Amparo López-Jiménez
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Nidia Isabel Molina-Gómez: Department of Environmental Engineering, Universidad Santo Tomás, 110231 Bogota, Colombia
Karen Rodríguez-Rojas: Department of Environmental Engineering, Universidad Santo Tomás, 110231 Bogota, Colombia
Dayam Calderón-Rivera: Department of Environmental Engineering, Universidad Santo Tomás, 110231 Bogota, Colombia
José Luis Díaz-Arévalo: Department of Civil and Agricultural Engineering, Universidad Nacional de Colombia, 111321 Bogota, Colombia
P. Amparo López-Jiménez: Hydraulic and Environmental Engineering Department, Universitat Politècnica de València, 46022 Valencia, Spain

Sustainability, 2020, vol. 12, issue 8, 1-20

Abstract: 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.

Keywords: urban sustainability; indicators; supervised classification; micro-territories (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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