WebMRT: An online tool to predict summertime mean radiant temperature using machine learning

• Published in: Sustainable cities and society 2024-11, Vol.115, p.105861, Article 105861
• Main Authors: AlKhaled, Saud R., Middel, Ariane, Shaeri, Pouya, Buo, Isaac, Schneider, Florian A.
• Format: Article
• Language: English
• Subjects: Empirical model; Heat stress; LightGBM; Mean radiant temperature; SAM; Urban climate
• ISSN: 2210-6707
• DOI: 10.1016/j.scs.2024.105861

•Introduced WebMRT, an online Tmrt prediction tool using tree-based ensemble models.•Trained on state-of-the-art measurements collected using Marty in Southwest US.•LightGBM achieved R2=0.92, RMSE=3.43, MAPE=5.33, and MBE = 0.20.•Streamlined process from input to Tmrt prediction for wider stakeholder engagement.•Designed with simplicity to enhance data-driven urban climate governance. Mean Radiant Temperature (Tmrt) is the most critical atmospheric variable influencing outdoor human thermal exposure and comfort in hot, dry environments. However, accurately quantifying Tmrt requires time-consuming field measurements with expensive equipment or complex, resource-intensive computations. We introduce WebMRT, an online tool to predict Tmrt using a data-driven approach. It features an intuitive interface using air temperature, shading status, and built environment features as predictors of Tmrt for a user-selected summer day, time, and location. Utilizing a tree-based ensemble model, WebMRT is trained on state-of-the-art human-biometeorological data collected by MaRTy using LightGBM after evaluating its performance against several candidate machine learning regressors. Feature engineering was applied to the day and time input, and two additional temporal features were derived: ‘Solar Altitude’ and ‘Minutes-from-Sunrise’. These inputs are integrated into the user interface, emphasizing simplicity and easy access for users at the frontend. After training the regressor on MaRTy datasets and employing k-fold cross-validation with ten folds, the model demonstrated strong predictive power (R2=0.92) with acceptable error (RMSE=3.43, MAPE=5.33) and bias (MBE=0.20). WebMRT also features optional fisheye photo uploads, processed using transfer learning techniques for image segmentation, further enhancing the tool's predictive accuracy, user experience, and applications towards climate action decision-making processes.