Earticle

초록

영어

Accurate and continuous environmental monitoring is a key element in implementing smart cities to protect civic health, optimize urban services, and make data-driven policy decisions. However, real-time, city-wide measurement of multidimensional environmental indicators such as CO₂, PM2.5, and VOCs requires the installation of large-scale physical sensors, which poses practical limitations such as cost, maintenance, and spatial constraints. To address this issue, this study proposes a multi-target regression-based soft sensor framework that simultaneously predicts multiple environmental indicators using readily available auxiliary data in cities, such as traffic volume, weather information, and population density. Using techniques such as Random Forest, LightGBM, and Multi-Output Regressor, we construct an integrated prediction model that considers the correlation between various output variables. Even in areas with limited monitoring stations, we achieve an average R² of over 0.80. The proposed model can be integrated with smart city public services, environmental policies, and real-time alert systems to enhance the efficiency and responsiveness of urban environmental management.

목차

Abstract
1. Introduction
2. Related Work
2.1 Research on Soft Sensors for Environmental Monitoring
2.2 Research Differences and Necessity
3. System Model
3.1 System Architecture
3.2 Problem Definition and Mathematical Modeling
4. Proposed Scheme
4.1 Overview
5. Performance Evaluation
5.1 Indicator-based comparison analysis
6. Conclusion
References

키워드

저자

• Ducsun Lim [ Research Fellow, Research Center, Korea Social Security Information Service, Korea ]
• Dongkyun Lim [ Professor, Department of Computer Science Engineering, Hanyang Cyber University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380