View Proposal

---

Proposer

Muhammad Zubai

Title

Air Quality Management Platform for Connected Smart Aktobe

Goal

To design, develop, and evaluate an intelligent software platform that predicts urban air quality and provides environmental decision support using machine learning techniques.

Description

Poor air quality can have significant environmental, social, and economic impacts in urban areas. Effective air quality management requires continuous monitoring, analysis of environmental conditions, and timely identification of potentially hazardous pollution levels. This project will develop a comprehensive air quality management platform for Connected Smart Aktobe. The system will integrate air quality sensor measurements with relevant environmental and urban data, including weather conditions and traffic information, to analyse pollution patterns and predict the Air Quality Index (AQI). Machine learning and predictive analytics techniques will be investigated to forecast AQI and identify periods or locations with potentially poor air quality. The platform will provide environmental decision-support capabilities by presenting air quality information, predictions, trends, and alerts through interactive dashboards. The proposed software architecture will include sensor and data acquisition modules, cloud-based data services, data processing and storage, predictive analytics services, visualization dashboards, and notification mechanisms. The system will be developed following the complete software engineering lifecycle, including requirements analysis, system design, implementation, testing, deployment, and evaluation. The performance of the proposed system will be evaluated using appropriate prediction and software-performance metrics. The project will also investigate how integrated environmental data and predictive analytics can support proactive air quality management and urban planning. The project is particularly relevant to the Connected Smart Aktobe concept, demonstrating how interconnected environmental data and intelligent software systems can support sustainable urban development and improved environmental decision-making.

Resources

[1] Garcia, A., Saez, Y., Harris, I. et al. (2025). Advancements in air quality monitoring: A systematic review of IoT-based air quality monitoring and AI technologies. Artificial Intelligence Review, 58, 275. https://doi.org/10.1007/s10462-025-11277-9 [2] Rosa-Bilbao, J., Butt, F. S., Merkl, D., Wagner, M. F., Schäfer, J., & Boubeta-Puig, J. (2025). IoT-Based Indoor Air Quality Management System for Intelligent Education Environments. IEEE Internet of Things Journal, 12(11), 18031–18041.

Background

Connected Smart Aktobe / Digital Kazakhstan initiatives and research on IoT-based air quality monitoring, predictive analytics, and intelligent environmental management.

Url

External Link

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Muhammad Zubai

Keywords

air quality, aqi prediction, machine learning, iot, environmental monitoring, predictive analytics, smart city, connected city, data visualization, sustainable aktobe, software engineering

Degrees

Bachelor of Science in Computational Sciences and Software Engineering