Rainfall Prediction at Ahmad Yani Meteorological StationUsing Integration ARIMA and LSTM

Abstract

Purpose: Predicting rainfall using ARIMA, LSTM, and Hybrid ARIMA-LSTM models to obtain accuracy values ​​on data at the Ahmad Yani Semarang station.

Methods: This study implements the ARIMA, LSTM, and hybrid ARIMA-LSTM models to determine which of these models produces the most significant predictions using rainfall data at the Ahmad Yani Meteorological Station in Semarang. This method proves whether using the hybrid ARIMA-LSTM, which is a combination of the two models, is able to provide greater accuracy compared to the ARIMA/LSTM model. The results of these predictions can certainly help relevant stakeholders to improve rainfall accuracy, especially at the Ahmad Yani Meteorological Station.

Result: By utilizing the power of statistical models (ARIMA) with deep learning (LSTM), the results of these two models provide higher accuracy compared to each model, as seen from the accuracy of the best ARIMA model using RMSE 15.8 and MAE 8.7, the best LSTM model RMSE 14.65 and MAE 9.06, while in the HYBRID ARIMA-LSTM model the best RMSE is 14.1 and MAE 9.06.

Novelty: This research adds to the knowledge regarding the accuracy or combination of ARIMA and LSTM models which are rarely used, especially in the world of meteorology or rainfall. By utilizing the ARIMA model which is able to read linear patterns and the LSTM model which reads non-linear patterns, the accuracy of rainfall increases and can help related stakeholders.