Deep Learning Models for the Prediction of Rainfall

Predicting torrential rainfall is a major challenge for meteorologists, affecting both lives and the economy. This study employs deep learning models like LSTM, GRU, Bi-LSTM, and hybrid LSTM-GRU to estimate rainfall. Results show that Bi-LSTM and hybrid LSTM-GRU models outperform key assessment metrics MSE, MAE, \(R^2\) , and RMSE. The paper aims to make rainfall forecast techniques more accessible to non-experts.