用强化学习找泰国土著雨季指数，提升长期降雨预测精度。
Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand
- 用深度Q网络自动选海温区域，优化北东北季风指数
- 12个气候区中多数地区预测误差显著降低，12个月前预报效果更优
- 适合做东南亚气候建模与灾害预警的研究者
长期降雨预测难度大。全球气候指数如厄尔尼诺-南方涛动虽常被用作机器学习输入，但泰国特定区域仍缺乏本地化气候指数以提升预测准确率。本文提出一种基于海表温度计算的新北东北季风气候指数，反映北半球冬季季风气候特征。为优化该指数的计算区域，采用深度Q网络强化学习代理，通过探索并选择与季节性降雨相关性最强的矩形区域。将降雨站划分为12个不同聚类，以区分泰国南部与北部降雨模式差异。实验结果表明，将优化后的指数引入长短期记忆模型后，在大多数聚类区域显著提升了长期月度降雨预测能力。该方法有效降低了12个月前瞻预报的均方根误差。
原文摘要 · Abstract (English)
Accurately predicting long-term rainfall is challenging. Global climate indices, such as the El Niño-Southern Oscillation, are standard input features for machine learning. However, a significant gap persists regarding local-scale indices capable of improving predictive accuracy in specific regions of Thailand. This paper introduces a novel North-East monsoon climate index calculated from sea surface temperature to reflect the climatology of the boreal winter monsoon. To optimise the calculated areas used for this index, a Deep Q-Network reinforcement learning agent explores and selects the most effective rectangles based on their correlation with seasonal rainfall. Rainfall stations were classified into 12 distinct clusters to distinguish rainfall patterns between southern and upper Thailand. Experimental results show that incorporating the optimised index into Long Short-Term Memory models significantly improves long-term monthly rainfall prediction skill in most cluster areas. This approach effectively reduces the Root Mean Square Error for 12-month-ahead forecasts.
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