Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Accurately predicting long-term rainfall is challenging. Global climate indices, such as the El Ni~no-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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 12 references that are in the map. First posted on arXiv 15 Jan 2026.
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References · 1
- 2015Continuous control with deep reinforcement learningcited by 3,407
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