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Smart city article details
| Title | Monitoring Hyderabad'S Carbon Emission Using Machine Learning For Sustainability Assessment Of Smart Cities |
|---|---|
| ID_Doc | 37861 |
| Authors | Gill K.S.; Reddy G.R.S.; Kumar M.; Rawat R.; Hsiung P.-A. |
| Year | 2024 |
| Published | 2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024 |
| DOI | http://dx.doi.org/10.1109/ISCS61804.2024.10581384 |
| Abstract | This research is about the assessment of Hyderabad City to reach SDG 11 goal, SDG(Sustainable development goal)11-Goal 11 is about making cities and human settlements inclusive, safe, resilient and sustainable. Today, more than half the world's population live in cities. By 2050, an estimated 7 out of 10 people will likely live in urban areas. As Hyderabad a upcoming mega city of India possible be the IT and Financial Capital of India and also the host to Countries defence and missile research centre DRDO, was already a big city but for the last two years this city name was spoken all over the world for being the best place to start up and invest because of having a good ecosystem with good logical support and was told will be a mega city and leading world IT by the year 2030 and will be growing more till 2050, so to make the city more sustainable development this paper will assess the past carbon emissions and also predict the future trends and also be predicting some Sustainable Development aspects like future transport pollutions and future road and transportation upgrade to be made in order to make the city resilient and make it go forward in an Sustainable manner. The research utilizes the Hyderabad city data from the year 1993 to 2019 and came up with the Emission Factor and Formulae to give the Carbon Estimates and made the Carbon estimates data set from the year 2002 to 2019 for the Transport sector and also predicted the vehicles growth and the carbon estimates till the year 2030. © 2024 IEEE. |
| Author Keywords | Artificial Intelligence; Classification; Classification Analysis; EF (Emission factors); Emissions; Machine Learning; Model Training; SDG11 |
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