Integrated machine learning models for enhancing tropical rainfall prediction using NASA POWER meteorological data

This research evaluates the performance of deep learning (DL) models in predicting rainfall in George Town, Penang, utilizing the open-source NASA POWER meteorological data, which includes variables such as rainfall, dew point, solar radiation, wind speed, relative humidity, and temperature. This study introduces a newly developed hybrid DL based on the integration of a 2D convolutional neural network (CNN2D) with a bidirectional recurrent neural network (BRNN) and a bidirectional gated recurrent unit (BGRU). The proposed models, CNN2D–BGRU and BRNN–BGRU, were compared against standalone models CNN2D, BRNN, and BGRU. The results indicate that the BRNN–BGRU model is the most effective, with a root mean square error (RMSE) value of 2.59, a mean absolute error (MAE) value of 1.97, a Pearson correlation coefficient (PCC) value of 0.79, and a Willmott index (WI) value of 0.88. In a 3-day prediction, the BRNN–BGRU model also performed the best, with a test WI value of 0.83, a PCC value of 0.69, a RMSE value of 3.02, and MAE value of 2.34. The hybrid BRNN–BGRU model consist-ently excels in predicting multi-step rainfall in tropical regions using the NASA POWER dataset. These findings can contribute to the development of advanced rainfall-predicting systems for more effective management of water resources and flooding in urban areas.