Sains Malaysiana 43(12)(2014): 1865–1871

Development of Generalized Feed Forward Network for Predicting Annual Flood (Depth)

of a Tropical River

(Pembangunan Rangkaian Suapan ke Hadapan Menyeluruh untuk Meramalkan

Banjir Tahunan (Kedalaman) Sungai Tropika)

MOHSEN SALARPOUR¹, ZULKIFLI YUSOP²*, MILAD JAJARMIZADEH¹ & FADHILAH YUSOF³

¹Faculty of Civil Engineering, Department of Hydraulic and Hydrology

Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

²Water Research Alliance, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

³Faculty of Science, Department of Mathematics, Universiti Teknologi Malaysia

81310 Skudai, Johor, Malaysia

Diserahkan: 17 Ogos 2013/Diterima: 16 April 2014

ABSTRACT

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable.

Keywords: Annual flood; artificial neural networks; cross validation; generalized feed forward; Johor River; predictive uncertainty

ABSTRAK

Pemodelan hubungan curahan hujan-aliran air di satu kawasan tadahan adalah sangat penting dalam mereka bentuk struktur hidraulik, mengawal banjir dan menguruskan air ribut. Rangkaian neural tiruan (ANNs) dikenal pasti mempunyai keupayaan untuk memperaga mekanisme tak linear. Kajian ini bertujuan untuk membangunkan model rangkaian suapan ke hadapan menyeluruh (GFF) untuk meramalkan banjir tahunan (kedalaman) Sungai Johor di Semenanjung Malaysia. Untuk mengelakkan latihan berlebihan, teknik pengesahan silang telah dijalankan bagi mengoptimumkan model tersebut. Di samping itu, indeks ketidakpastian ramalan digunakan untuk melindungi daripada pemparameteran berlebihan. Algoritma latihan pentadbiran adalah perambatan balik terma momentum dan jenis tangen hiperbolik digunakan sebagai fungsi perpindahan bagi lapisan tersembunyi dan output. Hasil kajian menunjukkan bahawa seni bina yang optimum diperoleh melalui fungsi perpindahan linear tangen hiperbolik bagi lapisan tersembunyi dan output. Nilai Nash dan Sutcliffe (NS) serta punca min ralat kuasa dua (RMSE) memperoleh 0.98 dan 5.92 bagi masa ujian. Penilaian pengesahan silang menunjukkan 9 proses elemen adalah mencukupi dalam lapisan tersembunyi untuk pengitlakan yang optimum dengan mengambil kira ramalan indeks ketidaktentuan yang diperoleh 0.14 dalam masa ujian adalah diterima.

Kata kunci: Banjir tahunan; ketidakpastian ramalan; pengesahan silang; rangkaian neural tiruan; suapan ke hadapan menyeluruh; Sungai Johor

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*Pengarang untuk surat-menyurat; email: zulyusop@utm.my