使用轻量级学习器集成进行降水预测
Precipitation Prediction Using an Ensemble of Lightweight Learners
AI总结：
针对降水时空模式复杂、强降水样本稀少的问题，提出含多个轻量级学习头与输出组合控制器的集成框架，并用三阶段方案分别训练，在 Weather4Cast 2023 核心测试和临近预报榜均获第一。
摘要：
降水预测在现代农业和工业中发挥着关键作用。然而，由于时空上多样的模式和动态变化，以及强降水事件的稀缺性，它带来了重大挑战。为应对这一挑战，我们提出了一个集成学习框架，利用多个学习器来捕捉降水分布的多样模式。具体而言，该框架由一个具有多个轻量级头（学习器）的降水预测器和一个组合这些头输出的控制器组成。学习器和控制器通过所提出的三阶段训练方案分别优化。通过利用提供的卫星图像，所提出的方法能够有效建模复杂的降雨模式，尤其是强降水事件。它在 Weather4Cast 2023 竞赛的核心测试以及临近预报排行榜上均获得第一名。有关详细实现，请参阅我们的 GitHub 仓库：https://github.com/lxz1217/weather4cast-2023-lxz。
英文摘要：
Precipitation prediction plays a crucial role in modern agriculture and industry. However, it poses significant challenges due to the diverse patterns and dynamics in time and space, as well as the scarcity of high precipitation events. To address this challenge, we propose an ensemble learning framework that leverages multiple learners to capture the diverse patterns of precipitation distribution. Specifically, the framework consists of a precipitation predictor with multiple lightweight heads (learners) and a controller that combines the outputs from these heads. The learners and the controller are separately optimized with a proposed 3-stage training scheme. By utilizing provided satellite images, the proposed approach can effectively model the intricate rainfall patterns, especially for high precipitation events. It achieved 1st place on the core test as well as the nowcasting leaderboards of the Weather4Cast 2023 competition. For detailed implementation, please refer to our GitHub repository at: https://github.com/lxz1217/weather4cast-2023-lxz.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
