用矩阵轮廓动态融合多流域模型，提升越南降雨预报精度与稳定性
Adaptive Rainfall Forecasting from Multiple Geographical Models Using Matrix Profile and Ensemble Learning
- 基于矩阵轮廓构建自适应加权集成框架，捕捉多模型间的协同变化
- 在5个预报时长、8个流域上误差均值和标准差均低于基准模型
- 适合需要高稳定性的洪水预警与水电调度场景
越南因气候多样、流域间地理差异显著，降雨预报极具挑战性，但准确可靠的预报对防洪、水电调度和灾害应对至关重要。本文提出一种基于矩阵轮廓的加权集成方法（MPWE），构建一种可动态切换的框架，能够捕捉多个地理模型预报间的协变依赖关系，并引入冗余感知加权机制以平衡各模型贡献。我们在越南八大主要流域的降雨预报数据上评估该方法，覆盖1小时及累计12、24、48、72、84小时共五个预报时长。实验结果表明，MPWE在所有流域和时长下均实现更低的预测误差均值与标准差，相比单个地理模型和集成基线，展现出更优的准确性与稳定性。
原文摘要 · Abstract (English)
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.
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