Long Short-Term Memory Algorithm for Rainfall Prediction Based on El-Nino and IOD Data

Abstract Rainfall has the highest correlation with adverse natural disasters. One of them, rainfall can cause damage to the hot mud embankments in Sidoarjo, East Java, Indonesia. Therefore, in this study, rainfall prediction is carried out to anticipate the damage to the embankments. The rainfall prediction was carried out using Long Short-Term Memory (LSTM) based on rainfall parameters: El-Nino and Indian Ocean Dipole (IOD). Experiments were carried out with two schemes: the first scheme used the El-Nino and IOD parameters, while the second scheme used rainfall time series pattern. Each scheme used varied number of hidden layers, batch size, and learn drop period. The prediction results using El-Nino and IOD parameters obtained MAAPE values ​​of 0.9644 with hidden layer, batch size and learn rate drop period values ​​of 100, 64, and 50. The prediction results using rainfall parameters resulted in a more accurate prediction with a MAAPE value of 0.5810. The best prediction results were obtained with the number of hidden layers, batch size and learn rate drop period of 100, 32, and 150 respectively.