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首页> 外文期刊>Theoretical and applied climatology >Comparison of the gene expression programming, artificial neural network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers
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Comparison of the gene expression programming, artificial neural network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers

机译：基因表达规划，人工神经网络（ANN）和多个分支河流洪水路由中的穆斯库文流入模型的比较

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摘要

Floods are considered as natural hazards. The parameter estimation of hydrologic methods is time-consuming in the flood routing of multiple inflows river systems. This paper presents the application of gene expression programming (GEP) and artificial neural network (ANN) as alternative approaches to predict the outflow hydrograph in downstream of multiple inflows systems. GEP and ANN models were compared with the equivalent Muskingum inflow model. The Gharesoo River Basin as a multiple inflows river system was applied for the calibration and verification phases of these models. The GEP obtained the formula as a function of the inflow branches based on the fitting data for simulating the outflow hydrograph of the multiple inflows system. GEP and ANN models investigated inflow hydrographs at different time steps. The obtained outflow hydrograph by the GEP model indicated an excellent performance compared with ANN and equivalent Muskingum inflow models in the case involving multiple inflows system.
机译：洪水被认为是自然危害。水文方法的参数估计是多次流入河流系统的洪水路由耗时。本文介绍了基因表达编程（GEP）和人工神经网络（ANN）作为替代方法，以预测多次流入系统下游流出的流水文。与相同的麝香流入模型进行比较GEP和Ann模型。 Ghareoo River盆地作为多流入河流系统用于这些模型的校准和验证阶段。基于用于模拟多流入系统的流出水文的拟合数据，GEP将该公式作为流入分支的函数。 GEP和Ann模型在不同的时间步骤中调查了流入文化。通过GEP模型的获得的流出水文显示出与涉及多个流入系统的ANN和等效的麝香顺序流入模型相比具有出色的性能。

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