用自适应专家混合模型提升降水预测精度。
Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction
- 按数据模态和时空模式分设专家，动态路由分配输入。
- 在飓风伊恩数据上优于所有基线模型，显著提升准确率。
- 适合气象、农业、灾害管理等需要精准降水预测的场景。
精确的降水预报对农业、灾害管理和可持续策略至关重要。然而，由于气候系统的复杂性以及雷达、卫星图像和地面观测等多源数据的异质性，降水预测仍具挑战。这些数据在时空分辨率和领域特征上差异显著，传统深度学习模型难以有效融合。现有研究虽尝试多种机器学习方法，但大多无法处理异构模态数据的整合。为此，我们提出一种面向降水率预测的自适应专家混合（MoE）模型。每个专家专注于特定模态或时空模式，动态路由机制则学习将输入分配给最相关的专家。实验结果表明，该模块化设计提升了预测准确率与可解释性。此外，我们开发了交互式可视化工具，支持用户直观探索历史天气时空模式，助力气候敏感领域的决策。评估基于2022年飓风伊恩期间的真实多模态气候数据集，基准结果表明，自适应MoE显著优于所有基线模型。
原文摘要 · Abstract (English)
Accurate precipitation forecasting is indispensable in agriculture, disaster management, and sustainable strategies. However, predicting rainfall has been challenging due to the complexity of climate systems and the heterogeneous nature of multi-source observational data, including radar, satellite imagery, and surface-level measurements. The multi-source data vary in spatial and temporal resolution, and they carry domain-specific features, making it challenging for effective integration in conventional deep learning models. Previous research has explored various machine learning techniques for weather prediction; however, most struggle with the integration of data with heterogeneous modalities. To address these limitations, we propose an Adaptive Mixture of Experts (MoE) model tailored for precipitation rate prediction. Each expert within the model specializes in a specific modality or spatio-temporal pattern. We also incorporated a dynamic router that learns to assign inputs to the most relevant experts. Our results show that this modular design enhances predictive accuracy and interpretability. In addition to the modeling framework, we introduced an interactive web-based visualization tool that enables users to intuitively explore historical weather patterns over time and space. The tool was designed to support decision-making for stakeholders in climate-sensitive sectors. We evaluated our approach using a curated multimodal climate dataset capturing real-world conditions during Hurricane Ian in 2022. The benchmark results show that the Adaptive MoE significantly outperformed all the baselines.
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