Artificial Intelligence Driven Air Quality Prediction for Sustainable Goa

Title

Artificial Intelligence Driven Air Quality Prediction for Sustainable Goa

Creator

Chandar S, Kumar

Description

Clean Air is essential for the health and survival of both humans and wildlife. Air pollution has been linked to various serious diseases, including cancer. Rapid industrial growth and increasing population have contributed to rising pollution from transportation, industries, and agriculture. As a result, air pollution has become a major issue, particularly in developing countries like India. To ensure good air quality, accurate and reliable monitoring and prediction are required. Machine Learning (ML) models have shown promise in predicting Air Quality Index (AQI) over traditional methods. This research aims to propose a AQI prediction model using Attention based Bi-directional Long Short-Term Memory (ABiLSTM) to predict AQI in various cities across Goa, India. Data processing methods are used to manage date before providing it into the ABiLSTM model. Daily AQI series from 2022 to 2024 for six cities in Goa- Panaji, Pond, Assanora, Codli, Tilamol, and Tuem are collected and utilized to verify the proposed model. Two models are tested, including BiLSTM and ABiLSTM. Experimental results showed that the ABiLSTM model outperformed BiLSTM model in all cities, reporting lower error values and higher R2 scores. A comprehensive analysis with a set of evaluation indices confirmed that the proposed ABiLSTM model effectively captures the characteristics of the original AQI series and achieves a higher accuracy in AQI prediction. 2025 IEEE.

Source

Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2025;pp.1181-1192

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Air Quality Index Prediction; Attention Based bidirectional Short-Term Memory; Deep Learning; Machine Learning

Coverage

Chandar S K., Christ University, School of Business and Management, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833150724-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAISS61471.2025.11041723

https://www.scopus.com/pages/publications/105010755728?origin=resultslist