Comparison of a neural network and a regression model to estimate suspended sediment in a semiarid basin
Comparison of a neural network and a regression model to estimate suspended sediment in a semiarid basin
复制标题
神经网络和回归模型估计半干旱盆地悬浮泥沙的比较
DOI：
--
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
M. Maneta
中科院分区：
文献类型：
--
作者：
S. Schnabel;M. Maneta
在流量短暂的小型半干旱盆地中，大部分沉积物输送发生在径流峰值超过一定流量期间。泥沙负荷通常使用拟合水-泥沙排放关系的额定曲线来建模。使用来自西班牙西南部树木繁茂的牧场 Parapunos 集水区的数据测试了前馈反向传播人工神经网络 (ANN) 和多重二次回归 (MQR) 模型的性能。两个模型均使用降雨和流量时间序列以及降雨强度、径流系数和流量变化率等衍生变量进行校准。分析中使用的最终变量集是基于 ANN 模型的敏感性分析和 MQR 模型中参数的统计显着性分析而完成的。 ANN 和 MQR 的性能相似，但优于单个变量的评级曲线。此外，ANN 和 MQR 可以重现泥沙-流量关系的滞后环。
In small semiarid basins with ephemeral flows most of the sediment conveyance takes place during runoff peaks exceeding a certain discharge. Sediment load is commonly modelled using rating curves fitting the water-sediment discharge relationship. The performance of a feed-forward back-propagation artificial neural network (ANN) and a multiple quadratic regression (MQR) model are tested using data from the Parapunos Catchment, a wooded rangeland located in SW Spain. Both models were calibrated using rainfall and discharge time series and derived variables such as rainfall intensity, runoff coefficient and rate of change of discharge. The final set of variables used in the analysis was done based on sensitivity analysis for the ANN model and based on an analysis of statistical significance of parameters in the MQR model. The performance of ANN and MQR were similar but better than rating curves of a single variable. In addition, ANN and MQR can reproduce the hysteretic loop of the sediment-discharge relationship.