Optimizing Air Pollution Prediction in Urban Environments using a Hybrid RNN-PBO Model with IoT Data

This research study proposes a novel hybrid model based on Recurrent Neural Network (RNN) combinatorial with Polar Bear Optimization (PBO) to improve air pollution prediction and control measures over cities. The model aims at learning the temporal relationships and interdependence among air-quality data. To implement the model styled with real-time Internet of Things (IoT) sensor data, tracking pollutants and other environmental factors in the urban region of study, the sequential nature of the RNNs allows it to build model patterns yet has a slow convergence and local minima when learning. To address this challenge, the PBO algorithm is introduced to optimize the parameter weight of the RNN, thus enhancing learning capacity and prediction accuracy. The exploration and exploitation of the PBO allows an effective mechanism of saving time with fast convergence and better process of complex data. The hybrid method was validated on an actual urban data set, giving improved prediction accuracy and stability over traditional RNNsand other optimization schemes. The proposed model aspires to deliver an efficient and trustworthy methodology for real-time air quality forecasting smart city management and a healthier city.