Flow discharge prediction in compound channels using linear genetic programming

Azamathulla, Hazi Mohammad; Zahiri, Abdolreza

doi:10.1016/j.jhydrol.2012.05.065

Cited by 52 publications

(15 citation statements)

References 23 publications

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“…Di Malaysia, kajian mengenai peramalan aliran sungai semakin berkembang. Kaedah pengaturcaraan persamaan genetik (Azamathulla & Zahiri 2012), Mesin Sokongan Vektor (Shabri & Suhartono 2012) dan kaedah pelicinan eksponen (Musa & Wan Mohamed 2007) merupakan antara kaedah yang digunakan untuk peramalan aliran sungai. Namun, peramalan proses hidrologi menggunakan teori kalut belum pernah diaplikasikan.…”

Section: Pengenalanunclassified

Predicting Time Series Data at Floodplain Area using Chaotic Approch (Peramalan Data Siri Masa Aliran Sungai di Dataran Banjir dengan Menggunakan Pendekatan Kalut)

Adenan¹,

Noorani²

2015

JSM

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Section: Pengenalanunclassified

Predicting Time Series Data at Floodplain Area using Chaotic Approch (Peramalan Data Siri Masa Aliran Sungai di Dataran Banjir dengan Menggunakan Pendekatan Kalut)

Adenan¹,

Noorani²

2015

JSM

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“…Azamathulla and Zahiri [24] used linear genetic programming to predict the ow discharge in a compound open channel. Zaji and Bonakdari [25] utilized the ANN and Radial Basis Neural Network (RBNN) to compute the discharge capacity of a triangular side weir.…”

Section: Introductionmentioning

confidence: 99%

Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming

2017

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Abstract. The determination of erosion and deposition patterns in channels requires detailed knowledge and estimation of the bed shear stress. In this investigation, the application of a Genetic Algorithm-based Arti cial (GAA) neural network and Genetic Programming (GP) was presented to predict bed shear stress in a rectangular channel with rough boundaries. Several input combinations, tness functions, and transfer functions were investigated to determine the best GAA model. Also, the e ect of various GP operators on estimating bed shear stress was studied. A comparison between the GAA and GP techniques' abilities to predict bed shear stress was made and then investigated. The results revealed that the GAA model performs better in predicting the bed shear stress (RMSE = 0.0774), as compared to the GP model (RMSE = 0.0835).

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“…Recently researchers used the number of the AI models for prediction flow discharge in the rivers. In this regard using the Artificial Neural Network, M 5 tree model, Gene Expression Method, SVM (Parsaie et al 2015c), and Group Method of Data Handling can be mentioned based on the reports the accuracy of the AI models is much more than the empirical approaches (Zahiri and Azamathulla 2014;Wang et al 2014;Al-Khatib et al 2013;Kozioł 2013;Yonesi et al 2013;Azamathulla and Zahiri 2012;Khatua et al 2012;Sahu et al 2011;Unal et al 2010;Knight et al 1984;. In this paper using the radial basis function neural network due to simple its structure for prediction of flow in compound open channel is considered.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of discharge of flow in compound open channel using radial basis neural network

Parsaie

Najafian

Shamsi

2016

Model. Earth Syst. Environ.

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Predicting the flow discharge in open channel is the main parameters in the flood management. The concept of the compound open channel is the accurate approach for modeling the natural streams. Several ways as analytical approaches and artificial intelligence methods have been proposed for predicting the discharge in rivers in term of compound open channel concepts. In this paper the single channel method (SCM), coherence method (COHM), and divided channel method (DCM) as common analytical approaches were used to predict the discharge in the compound open channel and in follow to achieve more accuracy in flow discharge prediction the radian basis neural network (RBF) was developed. The performance of RBF was compared with other types of transfer function governed on neurons of neural network. The results showed that the DCM with horizontal separated boundary among the subsections with correlation of determination (R 2 = 0.76) is accurate through the analytical approaches. Assessing the results of the MLP model showed that this model with (R 2 = 0.95) is a bit more accurate than the RBF (R 2 = 0.85) and analytical approaches.

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Flow discharge prediction in compound channels using linear genetic programming

Cited by 52 publications

References 23 publications

Predicting Time Series Data at Floodplain Area using Chaotic Approch (Peramalan Data Siri Masa Aliran Sungai di Dataran Banjir dengan Menggunakan Pendekatan Kalut)

Predicting Time Series Data at Floodplain Area using Chaotic Approch (Peramalan Data Siri Masa Aliran Sungai di Dataran Banjir dengan Menggunakan Pendekatan Kalut)

Mean bed shear stress estimation in a rough rectangular channel using a hybrid genetic algorithm based on an artificial neural network and genetic programming

Predictive modeling of discharge of flow in compound open channel using radial basis neural network

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