Track: Internet of Things |
| Street-level urban heat forecast and mapping using IoT-based weather sensors |
| Many real-time IoT applications exist in manufacturing, transportation, and agriculture, with less effort on climate and weather. Accurately modeling urban microclimates is challenging due to the high surface heterogeneity of urban land cover and the vertical structure of street morphology. The use of machine learning (ML) techniques and data from street-level IoT-based smart sensors have become popular in recent years as an emerging approach for urban climate studies. By providing real-time and precise weather conditions, these sensors can drastically improve the predictive capabilities of current weather forecast models for urban heat. This real-time monitoring and enhanced predictive capability enable more accurate and timely responses to urban climate challenges. In this study, we developed a modeling protocol that leverages state-of-the-art climate modeling techniques, high-precision lidar-based urban morphology, and real-time data streams from IoT sensors. The protocol was tested in Chicago to map air temperature at a hyper-local neighborhood scale. Additionally, we investigated sensitivity by comparing results from two machine learning algorithms: Gaussian Process Regression and Graph Neural Network, based on the nature of point-scale measurements by IoT sensors. We further tested the model’s reliability on out-of-sample locations to explore implications for feature engineering, data quality control, and strategic data collection. The improved predictive capabilities also contribute to better urban management and decision-making, enabling city planners to immediately optimize strategies of traffic management, emergency response, and public health monitoring. This study aims to help urban climate modelers effectively leverage emerging street-level observations in real-time, gain insights dynamically into next-gen urban climate modeling, and guide observation efforts to build a holistic understanding of urban microclimate dynamics. |
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| Presentation Video |
| Presentation Notes |
| LI-Street-level-urban-heat-forecast-and-mapping-using-IoT-based-weather-sensors.pptx |
Real Time Communications Conference & Expo at Illinois Tech
IEEE International Conference
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