A high spatiotemporal resolution framework for urban temperature prediction using IoT data

Accurate weather prediction, particularly accurate temperature prediction, is critical in decision-making for energy consumption, health risks, and economics. Regional numerical weather prediction (NWP) models produce operational-level temperature forecasts based on local atmospheric circulation conditions. They suffer from data- and computational intensity, resulting in low availability of high-spatiotemporal resolution. We embed a data fusion technique into the proposed framework to address this shortcoming, integrating measurements from the Internet of Things (IoT) with a high spatiotemporal resolution with weather stations observations. The framework utilizes a Long Short-Term Memory (LSTM) network to predict surface temperature from the fusion dataset for four major cities in the U.S. (Los Angeles, New York City, Atlanta, and Chicago). The predictive framework achieves an average RMSE of 1.72 °C and an average R² of 0.97 using the past 24 h to predict the future 12 h for Los Angeles (RMSE of 2.13 °C, 2.05 °C, 2.59 °C, and R² of 0.98, 0.98, 0.97 for New York City, Atlanta, and Chicago). The transfer learning is adopted to leverage the pre-trained model from regions with a higher number of observation stations to predict regions with fewer stations. The transferable model improved the predicting MAE for regions with data scarcity up to 26%.