Reinforcement Learning to Discover a North-East Monsoon Index for Rainfall Prediction in Thailand
This work addresses the problem of local-scale rainfall prediction for Thailand, offering a domain-specific incremental improvement by optimizing a climate index to enhance machine learning models.
The paper tackled the challenge of long-term rainfall prediction in Thailand by developing a novel North-East monsoon climate index optimized using reinforcement learning, which when integrated with LSTM models significantly improved 12-month-ahead forecast accuracy by reducing Root Mean Square Error in most cluster areas.
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.