Result Analysis of Air Pollution Forecasting using Deep Learning based LSTM-GRU Model
Keywords:
Long Term Short Memory (LSTM), Deep Learning (DL), Gate Recurrent Unit (GRU)Abstract
Air pollution has become a critical environmental and public health concern due to rapid urbanization, industrialization, increasing vehicular emissions, and changing climatic conditions. Accurate forecasting of air quality parameters is essential for effective environmental management, public health protection, and timely decision-making. This study presents a deep learning-based approach for air pollution forecasting using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. The proposed models are designed to capture complex temporal dependencies and nonlinear patterns in historical air quality and meteorological data. The performance of the LSTM and GRU models is evaluated using standard statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The comparative result analysis demonstrates the forecasting capability of both deep learning models and identifies the model that provides more accurate and reliable predictions. The experimental findings indicate that recurrent neural network-based models can effectively learn temporal variations in air pollution levels and provide improved forecasting performance. The proposed framework can support early warning systems, air quality monitoring, environmental planning, and public health management by enabling reliable prediction of future pollution trends.
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