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

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• 作者：Abbas Parsaie ; Shadi Najafian ; Zahra Shamsi
• 刊名：Modeling Earth Systems and Environment
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：2
• 期：3
• 全文大小：1,688 KB
• 刊物类别：Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Co
• 刊物主题：Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Applications in the Physical Sciences; Ec
• 出版者：Springer International Publishing
• ISSN：2363-6211
• 卷排序：2

文摘

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 (R2 = 0.76) is accurate through the analytical approaches. Assessing the results of the MLP model showed that this model with (R2 = 0.95) is a bit more accurate than the RBF (R2 = 0.85) and analytical approaches.KeywordsDischarge predictionFlood managementRiver engineeringArtificial neural network