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[2601.10181] Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand
Summary
This article presents a novel North-East monsoon climate index for improving rainfall predictions in Thailand, utilizing reinforcement learning to optimize input features.
Why It Matters
Accurate rainfall prediction is crucial for agriculture and disaster management in Thailand. This research addresses a significant gap in local-scale climate indices, potentially enhancing predictive models and informing better decision-making in climate-sensitive sectors.
Key Takeaways
- Introduces a new North-East monsoon climate index for rainfall prediction.
- Utilizes reinforcement learning to optimize input features for better accuracy.
- Demonstrates significant improvements in long-term rainfall predictions using LSTM models.
Computer Science > Machine Learning arXiv:2601.10181 (cs) [Submitted on 15 Jan 2026 (v1), last revised 19 Feb 2026 (this version, v5)] Title:Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand Authors:Kiattikun Chobtham View a PDF of the paper titled Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand, by Kiattikun Chobtham View PDF Abstract: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 1...
