Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments Titelbild

Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments

Shift Aware Transfer Learning with Adaptive Dual-Encoder Fusion for PM Forecasting in Data-Limited Environments

Jetzt kostenlos hören, ohne Abo

Details anzeigen
Forecasting air pollution (PM2.5) is hard where local sensor data is scarce, and naive transfer learning from data-rich regions can actually hurt performance due to domain mismatch. This study's dual-encoder framework pretrains on U.S. monitoring data, then adaptively fuses it with limited Taiwan-specific data, letting the source model adjust rather than freeze. The adapted model beat baselines significantly, with SHAP analysis confirming recent pollution levels and weather as key drivers. This has direct applications for environmental monitoring agencies in developing regions with sparse air quality infrastructure. Authors: Shahab Band, Hamed Mohammadi Paper: https://arxiv.org/abs/2608.14456v1
adbl_web_anon_alc_button_suppression_t1
Noch keine Rezensionen vorhanden