Comparative analysis and enhancing rainfall prediction models for monthly rainfall prediction in the Eastern Thailand

Rainfall prediction is a crucial aspect of climate science, particularly in monsoon-influenced regions where accurate forecasts are essential. This study evaluates rainfall prediction models in the Eastern Thailand by examining an optimal lag time associated with the Oceanic Niño Index (ONI). Five deep learning models—RNN with ReLU, LSTM, GRU (single-layer), LSTM+LSTM, and LSTM+GRU (multi-layer)—were compared using mean absolute error (MAE) and root mean square error (RMSE). A novel hybrid deep learning model was developed with respect to different conditions of the El Niño and Southern Oscillation (ENSO).

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  Our research compared the performance of five deep learning models in predicting monthly rainfall over five selected stations in the Eastern Thailand.
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  Different lag times were initially verified to optimize the time-interdependency between ONI and local meteorological parameters.
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  Our novel hybrid model demonstrated an improved accuracy across three distinct climate phases: El Niño, La Niña, and neutral events.