Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons
Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons
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DOI：
10.1016/j.advwatres.2003.10.003
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发表时间：
2004-02-01
影响因子：
4.7
通讯作者：
Cigizoglu, HK
中科院分区：
文献类型：
--
作者：
Cigizoglu, HK
河流悬沙量的确定，直接影响到许多水利工程的设计和运行，具有十分重要的意义。在这项研究中，多层感知器，MLP，最常用的人工神经网络算法在水资源文献中，在每日悬浮泥沙估计和预测的性能进行了研究。该研究的预测部分侧重于利用下游或上游站点过去的沉积物记录进行沉积物预测。研究的第二部分关注的是借助日平均流量估算沉积物值。从图表和统计数据可以看出，MLP比传统模型更好地捕捉到沉积物系列的复杂非线性行为。(C)2003 Elsevier Ltd.保留所有权利。
The determination of the suspended sediment amount on the rivers is of crucial importance since it directly affects the design and operation of many water resources structures. In this study the performance of multi-layer perceptrons, MLPs, the most frequently used artificial neural network algorithm in the water resources literature, in daily suspended sediment estimation and forecasting was investigated. The forecasting part of the study was focused on sediment predictions using the past sediment records belonging either to downstream or upstream stations. The estimation of sediment values with the help of daily mean flows was the concern of the second part of the study. From the graphs and statistics it is apparent that MLPs capture the complex non-linear behaviour of the sediment series relatively better than the conventional models. (C) 2003 Elsevier Ltd. All rights reserved.