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1. DESIGN IMPACT MOVEMENT
PRODUCT SHOWCASE
WORKBOOK
Mr. Shivanand Lamani
Mr. Raghavendra Kusoor
Mr. Suhas Pawar
Mr. Vivek Borgalle
TOPIC:
air quality prediction in
smart cities using machine
learning

2. Solution
Prototype Name : Air Quality Prediction in Smart
Cities using Machine Learning
One line description of the prototype:
Explain your prototype and its key features briefly:
The rapid urbanization of cities in Karnataka, India,
has led to an alarming deterioration in air quality,
posing significant challenges to public health and
environmental sustainability.
1. Data Acquisition and Preprocessing Module
2. AI-Driven Prediction Model
3. Integration with Smart City Frameworks
4. Web Application Development
DESIGNIMPACTMOVEMENT.TITAN.IN
Rapid urbanization has led to an increased focus on
monitoring and predicting air quality in smart cities

3. Solution
DESIGNIMPACTMOVEMENT.TITAN.IN

4. Solution
Additional slides to explain your prototype.
Insert your storyboard/sketches/images/videos of the model here.
DESIGNIMPACTMOVEMENT.TITAN.IN
• Machine Learning Prediction Model: Utilizes algorithms like Decision Trees, Random Forests, or
Neural Networks.Trained on a region-specific dataset for accurate air quality predictions.
• Integration Model with Smart City Frameworks: Develops connectors and APIs for seamless
data exchange.Ensures compatibility and interoperability with smart city management systems.
• Real-Time Data Update Model: Implements a mechanism using technologies like Web
Sockets. Ensures timely updates for web application and smart city frameworks.

5. Appendix
1. Smith, J. A., & Johnson, M. L. (2019). "Air Quality Prediction using Machine Learning
Models: A Comprehensive Review." Journal of Environmental Science, 45(2), 210-225.
2. Patel, J. K., & Brown, D. R. (2018). "Smart City Air Quality Monitoring and Management:
A Survey." IEEE Transactions on Sustainable Cities, 14(3), 450-465.
3. Chen, E. Y., & Wang, M. J. (2020). "Predicting Air Quality in Urban Environments with
Deep Learning Techniques." Environmental Modeling and Software, 35(4), 567- 583.
4. Gupta, R. K., & Sharma, P. S. (2021). "Integration of IoT and Machine Learning for Air
Quality Monitoring in Smart Cities." International Journal of Sensor Networks, 28(1),
78-92.
5. Harris, C. M., & Davis, M. L. (2015). "SQLite Database Management System: An Overview."
Journal of Database Systems, 22(2), 180-195.
6. Williams, R. C., & Taylor, A. J. (2018). "Machine Learning Applications in Environmental
Monitoring: A Systematic Literature Review." Environmental Research Letters, 33(4), 432-
445.
7. Gupta, S. M., & Singh, A. R. (2017). "Django Web Framework for Rapid Application
Development: A Review." Journal of Web Development, 25(3), 312-327.
8. Kumar, R. S., & Verma, P. R. (2019). "Comparative Analysis of Classification Algorithms
for Air Quality Prediction." Expert Systems with Applications, 41(6), 789- 805.