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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

A HYBRID AIR-WATER POLLUTION MONITORING SYSTEM USING OUTLIER DETECTION AND ADAPTIVE FEATURE SELECTION FOR AQI-BASED ENVIRONMENT ASSESSMENT

SRIKANTH M NAVINRAJ S GOKULRAJ S KUMAR S Dr. M. Balamurugan

REVATHI R

Department of Computer Science and Engineering The Kavery Engineering College Mecheri Salem-636453
DOI:https://doi.org/10.55041/ijcope.v2i4.596

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Air and water quality are vital indicators of environmental health, significantly affecting ecosystem stability and human well-being. While deep learning (DL) has shown strong predictive capabilities, its dependence on large-scale datasets, high computational complexity, and limited interpretability restricts its adoption in regulatory decision-support scenarios. This study proposes an intelligent rule-based framework incorporating an Outlier Detection and Removal Algorithm (ODRA) to enhance data reliability and a Threshold-Aware Feature Selection Algorithm (TAFSA) to identify influential parameters. The framework provides an accurate, transparent, and explainable alternative to complex black-box models for environmental monitoring.

How to Cite this Paper

M, S., S, N., S, G., S, K. & Balamurugan, M. (2026). A Hybrid Air-Water Pollution Monitoring System Using Outlier Detection And Adaptive Feature Selection For Aqi-Based Environment Assessment. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.596

M, SRIKANTH, et al.. "A Hybrid Air-Water Pollution Monitoring System Using Outlier Detection And Adaptive Feature Selection For Aqi-Based Environment Assessment." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.596.

M, SRIKANTH,NAVINRAJ S,GOKULRAJ S,KUMAR S, and M. Balamurugan. "A Hybrid Air-Water Pollution Monitoring System Using Outlier Detection And Adaptive Feature Selection For Aqi-Based Environment Assessment." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.596.

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References


  • Liu, S., et al. (2018). "Fault diagnosis of water quality monitoring devices." IEEE Access..

  • Kabir, S., et al. (2020). "Integrated belief rule base and deep learning." Sensors..

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  • Talagala, P. D., et al. (2019). "Feature-based procedure for detecting technical outliers." Water Resources Research..

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 27 2026
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