Pemodelan Curah Hujan-Limpasan Menggunakan Artificial Neural Network (ANN) dengan Metode Backpropagation
masih ada penyimpangan.
Abstract. Rainfall-runoff relation has been developed continuously by applying artificial intelligence as one of the black box model alternative called Artificial Neural Network. By applying black box model, it is not necessary to apply complexity of knowledge due to interrelated elements in a river basin in which it is not explicitly representing the relation of the elements and interaction process of the rainfall-runoff modeling. Consequently, the changes of the elements in a river basin is not necessary to be quantified as long as rainfall dan runoff is observed accurately from time to time, furthermore, the modeling can be applied within less complexity due to rainfall and runoff data observation as an input and output, respectively. The case study applied to the river flow on the way Sekampung River in Lampung Province. The data used is rainfall data and stream flow discharge data in the middle of the month on the water level station Pujorahayu, for 19 years from 1983 up to 2001. The rainfall data is input and stream flow is a variable output. Learning method that is used reduced gradient. From the result of this research got correlation coefficient 0,813 or 81,3% the tallest. The conclusion of this research is the generally ANN can implementated in the rainfall run off modeling, although the result is not extremely accurate yet because of the deviation between observation and result of the model.
Adidarma, W.K., Hadihardaja, I.K., Legowo, S., 2004, “Perbandingan Pemodelan Hujan-Limpasan Antara Artificial Neural Network (ANN) dan NRECA”, Jurnal Teknik Sipil ITB, Vol. 11 No.3, 105 – 115.
Ferianto, S.D., Hadihardaja, I.K., 2003, ”Pemodelan Multivariat Deret Waktu Sumberdaya Air Menggunakan Jaringan Saraf Buatan”, Jurnal Pengembangan Keairan Badan Penerbit Undip, 1 Tahun 10, Juli 2003, 58 – 75.
Hadihardaja, I.K., Sutikno, S., 2005, “Aplikasi Metode Generlized Reduced Gradient dalam Pemodelan Curah Hujan-Limpasan Menggunakan Artificial Neural Network”, Jurnal Media Komunikasi Teknik Sipil, Juni.
Hadihardaja, I.K., “Stream Flow Discharge and Relation Using Artificial Neural Network”, Jurnal Media Komunikasi Teknik Sipil, 10, 1 – 15. Hadihardaja, I.K., 2003, “Model Pengoperasian Waduk Tunggal dengan Jaringan Syaraf Tiruan”, Jurnal Pengembangan Keairan Badan Penerbit Undip, 2 Tahun 10, 2003 , 24 – 33.
Laurence, F., 1994, “Fundamental of Neural Networks”, Prentice Hall, Englewood cliffs, New Jersey.
Simon, H., 1994, “Neural Networks, A Comprehensive Fundation”, Macmillan College Publishing Company, USA.
Sri, K., 2003, ”Artificial Intelligence (Teknik dan Aplikasinya)”, Graha Ilmu. Vladan, B., Bojkov, H., Ventzi, 2001, “Runoff Modelling with Genetic Programming and Artificial Neural Networks”, D2K Technical Report, D2K TR 0401-1, April.
Vladan, B., Gopakumar, 1999, “Seabed Recognition Using Neural Networks”, D2K Technical Report, D2K TR 0399-1, Marc.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License