Optimizing Urban Heat Response with IoT-Driven Manhole Heat Detection using Recurrent Neural Networks

With the urban heat island effect amplifying the effects of warming temperatures, cities are having a harder and harder time keeping up. This research offers a novel solution to this critical problem by combining Recurrent Neural Networks (RNNs) with Internet of Things (IoT)-driven manhole heat monitoring devices. IoT sensors strategically placed across urban infrastructure can measure subsurface heat dynamics in real-time. After that, RNN models are used to forecast heat trends and make proactive treatments possible; these models may learn temporal connections. The effectiveness of the proposed method in improving heat response methods is shown using a thorough case study executed in a highly crowded metropolitan setting. The findings highlight the possibility of combining IoT technology with machine learning approaches to improve urban resilience and successfully deal with the problems caused by urban heat. More sustainable and suitable cities may result from creative solutions to the problems caused by urban heat and the existing body of understanding in that direction.