Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data
Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data
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DOI：
10.1016/j.jhydrol.2008.12.024
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发表时间：
2009-03-30
影响因子：
6.4
通讯作者：
Kisi, O.
中科院分区：
文献类型：
--
作者：
Cobaner, M.;Unal, B.;Kisi, O.
正确估算河流输沙量对许多水利工程具有重要意义。然而，常规的沉积物额定曲线不能提供足够准确的结果。本文提出了一种自适应神经模糊方法来估计河流悬浮泥沙浓度。以美国阿尔卡塔附近马德河流域的日降雨量、径流量和悬沙浓度资料为例进行了分析。在研究的第一部分中，目前的日降雨量，径流量和过去的日径流量，悬沙数据的各种组合被用作神经模糊计算技术的输入，以估计目前的悬沙。在研究的第二部分，神经模糊技术的潜力进行了比较，与三种不同的人工神经网络（ANN）技术，即广义回归神经网络（GRNN），径向基神经网络（RBNN）和多层感知器（MLP）和两种不同的泥沙定额曲线（SRC）。比较结果表明，对于本研究中使用的特定数据集，神经模糊模型在每日悬浮泥沙浓度估计方面优于其他模型。(C)2009 Elsevier B.V.保留所有权利。
Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, an adaptive neuro-fuzzy approach is proposed to estimate suspended sediment concentration on rivers. The daily rainfall, streamflow and suspended sediment concentration data from Mad River Catchment near Arcata, USA are used as a case study. In the first part of the study, various combinations of current daily rainfall, streamflow and past daily streamflow, suspended sediment data are used as inputs to the neuro-fuzzy computing technique so as to estimate current suspended sediment. In the second part of the study, the potential of neuro-fuzzy technique is compared with those of the three different artificial neural networks (ANN) techniques, namely, the generalized regression neural networks (GRNN), radial basis neural networks (RBNN) and multi-layer perceptron (MLP) and two different sediment rating curves (SRC). The comparison results reveal that the neuro-fuzzy models perform better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. (C) 2009 Elsevier B.V. All rights reserved.