Penggabungan Jaringan Syaraf Tiruan Dengan Model Driven Untuk Pengenalan Pola Data Berkorelasi Lemah  

Oleh:

Fauzi, Manyuk ; Anwar, Nadjadji ; Irawan, M. Isa ; Edijatno

Jenis: Article from Journal - ilmiah nasional - tidak terakreditasi DIKTI
Dalam koleksi: Gematika: Jurnal Manajemen Informatika vol. 7 no. 1 (Dec. 2005), page 1-12.
Topik: syaraf tiruan; JST; hidrology; debit - rain model; driven model; prediction accuracy

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: GG4.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The application of nerural network (JST) in hydrology relatively new. Recently debit - rain model using neural network not good enough to predict flood hydrography, that caused by relatively low relationthip between rain variable and debit variable. To increase the accuracy of rain variable prediction, hydrology expert used last debit as an input to predict flood hydrography. In this reasearch used combination of JST with driven model (conceptual model) increase prediction accuracy. In this reasearch shows accuracy predicion of flood hydrography location increase for the combination of JST with GR3J, where JST as driven data and GR3J is driven model.

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