Abstract
Daily rainfall forecasting is crucial in Indonesia. Because rainfall in Indonesia give rise to floods and landslides, affecting agriculture related to the adequacy of the amount of water on the ground, and affecting transportation, especially sea transportation and air transportation. Daily rainfall data in Indonesia is obtained from Meteorological, Climatological, and Geophysical Agency (BMKG Indonesia). In this research, the daily rainfall modeling was carried out using three methods, which are Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN) and Long Short Term Memory (LSTM). Based on the results of the ARIMA analysis, the model is not good enough because there are no models that meet the assumption of ARIMA. Therefore, rainfall data is analyzed using machine learning methods. The Machine Learning methods that used in this research are NN and LSTM. Based on the results of NN and LSTM, it can be concluded that the NN model is better than the LSTM model. This is due to the root mean square error (RMSE) value on the NN model is smaller than the LSTM model.
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Acknowledgments
This work is supported by Research and Technology Transfer Office, Bina Nusantara University as a part of Bina Nusantara University’s International Research Grant entitled Rainfall Modeling to Prevent Flooding in Jakarta using Machine Learning Method with contract number: No. 026/VR.RTT/IV/2020 and contract date: 6 April 2020.
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Permai, S.D., Ho, M.K. (2022). Daily Rainfall Analysis in Indonesia Using ARIMA, Neural Network and LSTM. In: Bourennane, S., Kubicek, P. (eds) Geoinformatics and Data Analysis. ICGDA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-031-08017-3_5
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