Comparative study of ML and DL techniques for AQI Prediction with Explainable AI

SANDEEP KAUR GORAYA; HINGARJOT KAUR

Comparative study of ML and DL techniques for AQI Prediction with Explainable AI

Author(s)	Ms. SANDEEP KAUR GORAYA, Dr. HINGARJOT KAUR
Country	India
Abstract	Abstract: The growing impacts of air pollution on public health, ecosystems, and climate have made the Air Quality Index (AQI) prediction a more critical research concern. Traditional statistical methods often fail to capture the nonlinear and complex relationships in AQI data. Machine learning (ML) and Deep learning (DL) techniques are becoming more and more popular due to their ability to process large amounts of data and to identify complex patterns. This paper highlights some machine and deep learning techniques, including Random Forest (RF), Support Vector Machines (SVM), and XGBoost, along with deep learning architectures, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), transformers. These models were mostly used because they are scalable, flexible, and better at combining data from multiple sources about air quality. The present study emphasizes comparative analysis, their strengths, weaknesses, application domains, and the trade-offs between computational cost and prediction accuracy. This paper focuses on air prediction through emerging technologies, including explainable AI alongside the integration of machine learning and deep learning techniques. The outcomes of this review are intended to guide the development of effective AQI prediction systems for real-world applications.
Keywords	Air Quality Index, Prediction, Machine Learning models, Deep Learning, Internet of Things (IoT)
Field	Computer Applications
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-23

View / Download PDF File

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Comparative study of ML and DL techniques for AQI Prediction with Explainable AI

Share this