用低成本雨量计+图神经网络，预测农村地区暴雨，适合缺设备地区。
Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges
- 用物联网雨量计收集数据，结合图神经网络捕捉降雨空间关系。
- 72个月日度数据验证，能有效提前预测强降雨事件。
- 适合气象站少、雷达覆盖差的欠发达地区使用。
准确及时地预测强降雨对洪水风险管理与防灾准备至关重要。通过本地化监测、分析和评估降雨数据，不仅能有效应对极端气候变化，还能优化地表与地下水资源规划。然而，发展中国家常因安装与维护成本高而缺乏连续数据采集的气象站。本文贡献有二：其一，提出一种低成本物联网系统，实现农村地区降雨的自动记录、监控与预测；其二，提出一种基于图神经网络（GNN）的新方法，可有效捕捉降雨模式中的复杂空间依赖关系。该方法在涵盖72个月、每日测量的历史数据集上进行了测试，实验结果证明其在预测强降雨事件上的有效性，特别适用于资源有限或传统气象雷达与站点覆盖稀疏的区域。
原文摘要 · Abstract (English)
Accurate and timely prediction of heavy rainfall events is crucial for effective flood risk management and disaster preparedness. By monitoring, analysing, and evaluating rainfall data at a local level, it is not only possible to take effective actions to prevent any severe climate variation but also to improve the planning of surface and underground hydrological resources. However, developing countries often lack the weather stations to collect data continuously due to the high cost of installation and maintenance. In light of this, the contribution of the present paper is twofold: first, we propose a low-cost IoT system for automatic recording, monitoring, and prediction of rainfall in rural regions. Second, we propose a novel approach to regional heavy rainfall prediction by implementing graph neural networks (GNNs), which are particularly well-suited for capturing the complex spatial dependencies inherent in rainfall patterns. The proposed approach was tested using a historical dataset spanning 72 months, with daily measurements, and experimental results demonstrated the effectiveness of the proposed method in predicting heavy rainfall events, making this approach particularly attractive for regions with limited resources or where traditional weather radar or station coverage is sparse.
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