earticle

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.