Hybrid models Cloud-based Enhancements for Air Quality Prediction Systems

Dhanawade, Chinmay Vijay (2024) Hybrid models Cloud-based Enhancements for Air Quality Prediction Systems. Masters thesis, Dublin, National College of Ireland.

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Abstract

This paper analyzes the creation and implementation of a hybrid model for air quality forecasting that incorporates statistical and machine learning techniques within the cloud computing paradigm. A major goal was to use the SARIMA (Seasonal AutoRegressive Integrated Moving Average) and Random Forest models with the aim of increasing predictive accuracy and reliability. First, LSTM could not be introduced to the initial trials in Cloud9 as the tool didn’t have TensorFlow - a prerequisite for running and integrating LSTMs into one model. Key results thus conclude that the SARIMA combined with Random Forest has indeed been deployed in the cloud, as the program developed in Google Colab proved successful. The deployment process involved linking the application to GitHub, which triggered a pipeline that facilitated the deployment to Elastic Beanstalk. Such a process underscores the role of cloud computing in dealing with large datasets and enabling real-time data processing for air quality predictions. The study points out the critical role of efficient model integration and cloud infrastructure in dealing with environmental challenges. This research contributes to the field of environmental monitoring by demonstrating that traditional statistical models can be effectively combined with machine learning techniques. The results suggest that such hybrid approaches can significantly improve decision-making related to public health and environmental policy, ultimately fostering more sustainable urban development practices.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Deshmukh, Sudarshan UNSPECIFIED
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	15 Jul 2025 08:26
Last Modified:	15 Jul 2025 08:26
URI:	https://norma.ncirl.ie/id/eprint/8095

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