A Novel Digital Twin Framework for Adaptive Urban Weather Visualization Using IoT and Fuzzy Logic

This research identifies the urgent need for adaptive and real-time urban weather visualization models to support city planning and decision-making.The proposed solution involves the use of digital twin technology integrated with the Fuzzy Sugeno method to handle dynamic and complex environmental data.The novelty of this approach lies in its ability to provide real-time, adaptive visualizations through a combination of IoT (Internet of Things) data and advanced fuzzy logic processing, enhancing the accuracy and relevance of urban weather monitoring.This method enables the system to process real-time weather data collected from IoT sensors distributed throughout the city, resulting in accurate and relevant visualizations.The weather data, including temperature, humidity, and wind speed, is sent to a data centre where the Fuzzy Sugeno method is used to transform this data into visualizable weather information.The weather visualizations include conditions such as heavy rain, regular rain, drizzle, overcast, cloudy, sunny cloudy, somewhat cloudy, and sunny.The results of this study demonstrate that the system can display weather conditions adaptively and in real-time, significantly contributing to the understanding and management of urban heat islands.In ten experiments with temperature ranges from 21C to 34C, humidity from 55% to 87%, and wind speeds from 12 km/h to 32 km/h, the dominant defuzzification value was 0.6, corresponding to sunny cloudy weather conditions.The implementation of this system also shows improvements in the quality and accuracy of weather information presented, thereby aiding in more responsive and sustainable urban planning.