<![CDATA[Air quality deterioration has become a major challenge for public health and environmental management in urban areas. Internet of Things (IoT)-based monitoring systems continuously generate sensor data that can be exploited for air quality prediction; however, these datasets commonly contain missing values, noise, and temporal dependencies that may reduce prediction accuracy. This study proposes a Long Short-Term Memory (LSTM)-based model to predict carbon monoxide (CO(GT)) concentrations using the Air Quality UCI dataset, which consists of 9,357 observations and 15 attributes. During preprocessing, -200 values were identified as missing-value indicators, followed by invalid-data handling, Min-Max normalization, and sequence generation using a sliding-window approach with a window size of four. The processed data were divided into training and testing sets using an 80:20 ratio. The prediction model employs a single LSTM layer with 50 hidden units and a Dense output layer and is trained using the Adam optimizer for 50 epochs. Experimental results achieved a Mean Absolute Error (MAE) of 0.0389 and a Root Mean Squared Error (RMSE) of 0.0567, indicating that the proposed model effectively captures temporal patterns in air quality observations with relatively low prediction errors. These findings are consistent with previous studies reporting the effectiveness of LSTM for air quality forecasting and demonstrate its potential to support continuous IoT-based environmental monitoring systems. Future work may incorporate hyperparameter optimization and comparative evaluations with alternative deep learning architectures to further improve predictive performance.]]>
