Machine Learning-Driven Predictive Models for Urban Sustainability in the Context of Digital Transformation

Authors

  • Shamina Israr Tithi * Earth and Environmental sciences, Brooklyn College, CUNY, USA.

https://doi.org/10.48313/iee.v1i2.42

Abstract

Societal considerations make sustainable urban planning in the age of energy and digital revolution paramount. By using state-of-the-art methods for analyzing massive datasets, such as Artificial Intelligence (AI) and Machine Learning (ML), we may get a better grasp of past data and more effectively forecast future occurrences using information gathered from IoT devices. The effects of energy transformation and environmental policy were examined, as were the long-term consequences of specific activities, using a multi-dimensional historical analysis of air pollution that this research used. Predictions of air pollution were also made using ML methods that included geographical considerations. Incorporating data from numerous sites and assessing the effect of neighboring sensors on predictions, this research used many low-cost air sensors categorized as Internet of Things (IoT) devices. Regression models, Deep Neural Networks (DNNs), and ensemble learning were among the ML techniques examined. There was an investigation into the feasibility of using such forecasts in open-source IT mobile systems. The study took place in Kraków, Poland, a city with a long history of air pollution and a UNESCO World Heritage Site. Additionally, Kraków is in charge of creating clean mobility zones and banning the use of solid fuels for heating. According to the study, increasing the city's population has no negative effect on PMx concentrations. The main aspect in bettering air quality, particularly for PMx, is shifting from coal-based to sustainable energy sources. Transportation has a less significant impact. Using linear ML models yields the best results when attempting to forecast infrequent smog episodes. Building a smart city that considers the effects of air pollution on quality of life may be greatly advanced by acting on the findings of this study.   

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Published

2025-06-04

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Vol. 1 No. 2 (2025): Innovations in Environmental Economics (IEE)

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Articles

How to Cite

Machine Learning-Driven Predictive Models for Urban Sustainability in the Context of Digital Transformation. (2025). Innovations in Environmental Economics , 1(2), 96-108. https://doi.org/10.48313/iee.v1i2.42