Hybrid informer–BiLSTM Model for Long-Term Forecasting of Urban Heat Islands: A Multimodal Approach Integrating Remote Sensing and Climate Data

Urban heat islands (UHIs) localized zones of increased temperature pose escalating challenges in densely populated regions due to accelerated urbanization and climate variability. Accurate long-term forecasting of UHI dynamics is critical for sustainable urban planning and climate adaptation. Here, we present a novel hybrid forecasting framework integrating Informer and Bidirectional Long Short-Term Memory (BiLSTM) networks to model long-term UHI intensity trends. The hybrid architecture leverages the Informer's strength in capturing global temporal dependencies and BiLSTM’s ability to recognize bidirectional sequential patterns in high-resolution, multimodal data derived from Landsat-8, MODIS, and ERA5 datasets. Our framework predicts land surface temperature (LST) anomalies used as a proxy for UHIs across 20 megacities globally. The model demonstrates significant performance improvements over existing benchmarks, achieving a mean RMSE of 1.13°C, MAE of 0.91°C, and an R² of 0.93. The spatial heterogeneity of model performance reveals higher forecast accuracy in arid zones versus coastal or monsoon-influenced urban areas. This work offers a robust, scalable tool for proactive climate resilience in rapidly urbanizing environments.