Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.08277 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916947165184000 |
|---|---|
| author | Tran, Dung T. Huyen, Huyen Ngoc Nguyen, Hong Phan, Xuan-Vu Nguyen, Nam-Phong |
| author_facet | Tran, Dung T. Huyen, Huyen Ngoc Nguyen, Hong Phan, Xuan-Vu Nguyen, Nam-Phong |
| contents | Rainfall forecasting in Vietnam is highly challenging due to its diverse climatic conditions and strong geographical variability across river basins, yet accurate and reliable forecasts are vital for flood management, hydropower operation, and disaster preparedness. In this work, we propose a Matrix Profile-based Weighted Ensemble (MPWE), a regime-switching framework that dynamically captures covariant dependencies among multiple geographical model forecasts while incorporating redundancy-aware weighting to balance contributions across models. We evaluate MPWE using rainfall forecasts from eight major basins in Vietnam, spanning five forecast horizons (1 hour and accumulated rainfall over 12, 24, 48, 72, and 84 hours). Experimental results show that MPWE consistently achieves lower mean and standard deviation of prediction errors compared to geographical models and ensemble baselines, demonstrating both improved accuracy and stability across basins and horizons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08277 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Rainfall Forecasting from Multiple Geographical Models Using Matrix Profile and Ensemble Learning Tran, Dung T. Huyen, Huyen Ngoc Nguyen, Hong Phan, Xuan-Vu Nguyen, Nam-Phong Machine Learning Rainfall forecasting in Vietnam is highly challenging due to its diverse climatic conditions and strong geographical variability across river basins, yet accurate and reliable forecasts are vital for flood management, hydropower operation, and disaster preparedness. In this work, we propose a Matrix Profile-based Weighted Ensemble (MPWE), a regime-switching framework that dynamically captures covariant dependencies among multiple geographical model forecasts while incorporating redundancy-aware weighting to balance contributions across models. We evaluate MPWE using rainfall forecasts from eight major basins in Vietnam, spanning five forecast horizons (1 hour and accumulated rainfall over 12, 24, 48, 72, and 84 hours). Experimental results show that MPWE consistently achieves lower mean and standard deviation of prediction errors compared to geographical models and ensemble baselines, demonstrating both improved accuracy and stability across basins and horizons. |
| title | Adaptive Rainfall Forecasting from Multiple Geographical Models Using Matrix Profile and Ensemble Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.08277 |