Aplikasi Model Wavelet Neuro Fuzzy Untuk Memprediksi Banjir Sungai Bengawan Solo

  • Nurmalitasari Nurmalitasari STMIK Duta Bangsa
  • Sri Sumarlinda STMIK Duta Bangsa
Keywords: Forecasting, Bengawan Solo River, Wavelet neuro fuzzy, MSE

Abstract

 The objectives of this research is to implementation wavelet neuro fuzzy method to predict water level of Bengawan Solo river. The wavelet neuro fuzzy method is a model combination between discrete wavelet transformation, Artificial Neural Network (ANN) and fuzzy logic. Wavelet Neuro fuzzy modeling aims to reduce the weaknesses of each system, and combine existing advantages of each system, so the predicted result has a very small error value. Predicted when the flood is important because the predicted result can provide early warning information to the community around the river when the arrival of floods so as to reduce the risk of disaster and prepare for emergency response action. The data used in this research are high level of water level data obtained from AWLR Serenan post. The results of the wavelet neuro fuzzy method show the Mean Square error (MSE) forecast of 0.0613.

References

B. N. P. B. RI, “Data Bencana Indonesia,” Jkt. BNPB, 2015.

A. Findayani, “Kesiapsiagaan Masyarakat Dalam Penanggulangan Banjir Di Kota Semarang,” J. Geogr. Media Infromasi Pengemb. Ilmu Dan Profesi Kegeografian, Jan. 2015.

Dewan Perwakilan Rakyat Republik Indonesia and Presiden Republik Indonesia, Undang-Undang Republik Indonesia Nomor 24 Tahun 2007 Tentang Penanggulangan Bencana Dengan Rahmat Tuhan Yang Maha Esa Presiden Republik Indonesia. .

Y. Bodyanskiy, I. Pliss, and O. Vynokurova, “Adaptive wavelet-neuro-fuzzy network in the forecasting and emulation tasks,” Int J. Inf. Theory Appl., vol. 15, no. 1, pp. 47–55, 2008.

Fereydooni, M. and Pezhman, S, “Use Of Hybrid Wavelet-Neural And Wavelet Neuro-Fuzzy Model In Simulation Of Rate Of Flow Of River (Study Case: Fahlian River),” Indian J. Fundam. Appl. Life Sci., vol. 5, no. 3, pp. 692–701, 2015.

Ö. Kişi, “Streamflow forecasting using different artificial neural network algorithms,” J. Hydrol. Eng., vol. 12, no. 5, pp. 532–539, 2007.

L. Fausset, “Fundamentals of neural networks,” Archit. Algorithm Appl. Prentice Hall, 1994.

Nabizadeh M, Mosaedi A, and Dehghani A, “Intelligent estimation of rate of flow by utilizing ANFIS (Adaptive-Network-Based Fuzzy Inference System),” Water Irrig. Manag., vol. 2, no. 1, pp. 69–80, 2011.

Sielvy E., “Aplikasi Model Neuro Fuzzy untuk Memprediksi Harga Emas,” Skripsi, UNY, Yogyakarta, 2013.

A. Setiaji, “Aplikasi model wavelet-neuro-fuzzy untuk memprediksi nilai tukar euro terhadap dollar amerika,” PhD Thesis, UNY, 2014.

C.-T. Lin and C. G. Lee, “Neural fuzzy systems,” PTR Prentice Hall, 1996.

Published
2018-06-06
How to Cite
Nurmalitasari, N., & Sumarlinda, S. (2018). Aplikasi Model Wavelet Neuro Fuzzy Untuk Memprediksi Banjir Sungai Bengawan Solo. NUMERICAL: Jurnal Matematika Dan Pendidikan Matematika, 2(1), 21-30. https://doi.org/10.25217/numerical.v2i1.218
Section
Articles