Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM)

  • Retzi Yosia Lewu (1) Universitas Amikom Yogyakarta
  • Slamet Slamet (2) Universitas Amikom Yogyakarta
  • Sri Wulandari (3) Universitas Amikom Yogyakarta
  • Widdi Djatmiko (4) Universitas Amikom Yogyakarta
  • Kusrini Kusrini (5) Universitas Amikom Yogyakarta
  • Mulia Sulistiyono (6*) Universitas Amikom Yogyakarta

  • (*) Corresponding Author
Keywords: Rainfall, Water Discharge, Forecasting, Flood, Long Short Term Memory (LSTM)


Flood disasters can occur at any time when the factors for the amount of river water discharge and rainfall intensity tend to be high, so preparations and ways of handling are needed to anticipate flood disasters quickly, precisely, and accurately for the Surabaya Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is by calculating predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict rainfall and river water discharge on the Jagir River in Surabaya. The LSTM method is a model commonly used for predictions based on time series data. The data obtained are rainfall data and water discharge on the Jagir River, Surabaya, which will be used as training and testing data to make predictions. The results of implementing the LSTM method using data training of 70% and data testing of 30% on rainfall data using the best epoch, namely at epoch ten by producing tests on data testing can have a Mean Absolute Error (MAE) performance of 4.5 and Root Mean Square Error (RMSE) of 9.7. Whereas the water discharge variable uses the best epoch, namely at epoch 75, by producing data testing data which can have a Mean Absolute Error (MAE) performance of 11.49 and a Root Mean Square Error (RMSE) of 9.63.



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How to Cite
Lewu, R., Slamet, S., Wulandari, S., Djatmiko, W., Kusrini, K., & Sulistiyono, M. (2023). Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM). Jurnal Riset Informatika, 5(3), 439-446.
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