IoT-Enabled Hybrid SVM–Random Forest-Based Model for Real-Time Air Pollution Prediction in Smart Cities

In smart cities, air pollution has grown to be a serious problem that has an impact on both environmental sustainability and human health. In order to predict air pollution in real time, this study suggests an Internet of Things-enabled hybrid model that combines Random Forest (RF) and Support Vector Machine (SVM). IoT sensors are used to gather environmental data, including temperature, humidity, and particle matter. The hybrid model makes use of RF’s ability to increase prediction accuracy through ensemble learning and SVM’s ability to handle high-dimensional input. With an accuracy of 96.5% and a lower RMSE of 0.24, experimental findings show that the suggested model performs better than conventional machine learning models. The technology helps decision-makers put timely control measures into place by facilitating effective monitoring and early pollution level forecast. This method helps create intelligent and sustainable urban landscapes while improving prediction dependability.