Micro-scale Urban Heat Island Analytics with Anthropogenic Heat Releases and A Machine Learning Enabled Forecasting Model

Authors: Jingchao Yang*, George Mason University, Manzhu Yu, Pennsylvania State University Main Campus, Chaowei Yang, George Mason University
Topics: Geographic Information Science and Systems, Urban and Regional Planning
Keywords: Urban Heat Island, Machine Learning, Foresast
Session Type: Paper
Presentation File: No File Uploaded


Urban Heat Island (UHI) can affect human health and imbalance urban energy usage, the global warming trend is deteriorating the UHI by increasing the already higher temperatures in heat island areas. Most researches have been relying on remote sensing imagery or sparsely distributed station sensor data and focusing on the broad understanding of the meso- or city- scale UHI phenomenon and mitigation support. However, challenges remain due to the complex nature of UHI, including the understanding of such phenomenon (i.e., urban energy exchange) and data collections, e.g., meteorological parameters, urban geometry, and human activities. This project aims to: 1) build an in-depth investigation of the human-weather-climate relations for the urban area; 2) fill the gap between short-term weather impact effects from buildings, traffics, human mobilities, and long-term microclimate from understanding such relations with real-time urban sensing (IoT) data; 3) establish a machine-learning enabled ensemble model for fast near-future temperature forecasts by considering the human-weather-climate relationships. The outcomes from the study are expected to provide a guideline for the precautionary local-human-activity management strategy design and implementation to reduce public health-related risks, allowing better urban living spaces.

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