Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso

Authors

  • Afan Galih Salman Bina Nusantara University
  • Yen Lina Prasetio Bina Nusantara University

DOI:

https://doi.org/10.21512/comtech.v1i2.2384

Keywords:

Artificial Neural Network (ANN) Recurrent Elman, ENSO, coefficient of determination (R2), Root Mean Square Error (RMSE), Gradient Descent Adaptive Learning Rate.

Abstract


The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3.

 

Dimensions

Plum Analytics

References

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Published

2010-12-01

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