20.500.12556/DKUM-98514 Location-aware transfer learning for air quality time-series prediction Accurate short-term PM10 forecasting is difficult because pollutant dynamics vary across monitoring locations, while sufficient target-station history is not always available due to new deployments, sensor outages, or quality-control filtering. Spatial cross-station transfer learning addresses this problem by pre-training a temporal model on data-rich source stations and fine-tuning it on a data-scarce target station. However, ordinary transfer learning may suffer from source–target domain mismatch and often does not explicitly condition the transferred model on station-specific spatial context, whereas graph-based spatio-temporal models typically require a predefined station graph, synchronized network-level inputs, or assumptions about spatial connectivity. This study therefore examines whether location-aware conditioning improves LSTM-based cross-station transfer learning for one-step-ahead PM10 forecasting under different target-data budgets. The proposed HybridLocLSTM extends a two-layer LSTM backbone with station-identity and geographic-coordinate embeddings, which are fused with the temporal representation. We evaluate seven approaches across 21 Slovenian PM10 monitoring stations and six target-data budgets. The results show that location-aware conditioning improves transfer learning relative to plain LSTM transfer across all evaluated scarcity levels achieving the lowest mean MAE and the best average rank. These findings indicate that explicit station-level spatial conditioning provides the most consistent performance across data regimes, particularly when target-station data are limited. transfer learning location-aware learning LSTM air quality time-series prediction prenos učenja kvaliteta zraka napoved časovnih vrst true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/electronics © 2026 by the authors Članek v reviji 2026-06-17 15:54:06 2026-06-17 15:54:06 2026-06-18 03:25:30 0000-00-00 00:00:00 2026 0 0 26 str. iss. 11, [article no.] 2470 Vol. 15 Jun. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-04-25 2026-06-01 2026-06-04 004.8 281310723 10.3390/electronics15112470 2079-9292 https://www.mdpi.com/2079-9292/15/11/2470 1 02559742-6a54-11f1-87c9-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=207259 electronics-15-02470-v2.pdf electronics-15-02470-v2.pdf 1 5D0662D6C7D21650E3B9FA4D735B6D50 bf23f3d8da7a1da8cebcb5a8a5b96bb4016c74b9d9c1178cc7be526869f0e99b 3b433e75-6a54-11f1-87c9-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=207260 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0