Smart City Gnosys
Smart city article details
| Title | Artificial Intelligence-Based Co2 Emission Predictive Analysis System |
|---|---|
| ID_Doc | 10554 |
| Authors | Yeasmin S.; Syed S.N.J.; Shmais L.A.; Dubayyan R.A. |
| Year | 2020 |
| Published | 2020 International Conference on Artificial Intelligence and Modern Assistive Technology, ICAIMAT 2020 |
| DOI | http://dx.doi.org/10.1109/ICAIMAT51101.2020.9307995 |
| Abstract | Smart cities aim for the optimization and enhancement of all domains of application, as well as strive for more sustainable ecosystems. Emerging and disruptive technologies such as Artificial Intelligence are transforming the way we uncover and bring value, making them optimal when it comes to building and maintaining a smart city. Artificial Intelligence has become a driving force behind many technological innovations and proved significant in different fields including medicine, education, marketing, food technology, banking, and green technology. Nowadays, people are more aware of pressing matters concerning the environment. They are more willing to opt for environmentally friendly solutions wherever possible. We aim to introduce a solution by which people's actions to limit environmental damage are not limited to merely purchasing ecofriendly products but extended to getting those products delivered in a way that is least harmful to the planet. Therefore, this paper proposes a CO2 emission predictive analysis using Artificial Intelligence that calculates and predicts the amounts of CO2 to be emitted from a cargo van in advance. The system takes into account the total distance to be traveled and the amount of gas needed to travel that distance and predict the emission accordingly. This paper proposes that such a system should also be embedded with self-driving cars, as in the smart cities of the future self-driving cars will become the norm. Relevant conceptual information used to develop the predictive analysis system is discussed. A discussion is also made on the development of the system as well as the preparation of the dataset. The system is analyzed based on the accuracy of the prediction. Finally, a future direction for the current approach of implementation is presented. © 2020 IEEE. |
| Author Keywords | Artificial Intelligence; Carbon Emissions; Multivariable Linear Regression; Predictive Analysis; Smart Cities; Supervised Learning |
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