IoT-Enabled Green Roof Management with RNN-Based Dynamic Environmental Control

Abstract

This research presents a dynamic environmental management system for urban landscapes that leverages Recurrent Neural Networks (RNNs) in the context of IoT-enabled green roof management. Green roofs play a pivotal role in promoting urban sustainability by improving air quality, mitigating urban heat island effects, and enhancing energy efficiency. The proposed system integrates a network of environmental sensors—measuring temperature, humidity, light intensity, soil moisture, and air quality—to continuously monitor and optimize the performance of green roofs. The collected sensor data is processed using an RNN model, which is particularly well-suited for this application due to its ability to capture temporal dependencies and trends in sequential data. By forecasting environmental conditions, the RNN provides insights into the operational health and effectiveness of green roofs. Moreover, the system supports real-time decision-making by offering adaptive recommendations for controlling ventilation, lighting, and irrigation systems. This dynamic feedback mechanism not only reduces energy consumption but also strengthens the overall sustainability of urban environments. Experimental evaluations demonstrate that the RNN-based approach achieves superior accuracy, precision, and recall compared to conventional models. These findings highlight the potential of combining IoT technologies with advanced machine learning to optimize urban green infrastructure. The study underscores the broader significance of such intelligent systems in advancing sustainable urban development and in enhancing the resilience of cities to environmental challenges.