Coupling SWAT and ANN models for enhanced daily streamflow prediction
Coupling SWAT and ANN models for enhanced daily streamflow prediction
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DOI：
10.1016/j.jhydrol.2015.11.050
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发表时间：
2016-02-01
影响因子：
6.4
通讯作者：
Kalin, Latif
中科院分区：
文献类型：
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作者：
Noori, Navideh;Kalin, Latif
为提高无监测流域日径流预报的精度，将准分布式流域模型与人工神经网络相结合，建立了一种混合模型。利用美国东南部亚特兰大市及其周边29个流域的日径流资料，采用留一点切出技术，建立了暖季和冷季的径流预测模型。每日径流量首先模拟与土壤和水评估工具（SWAT），然后SWAT模拟基流和暴雨流量被用作输入到ANN。在总的29个测试流域，其中62%和83%的Nash-Sutcliffe值在0.50以上，在凉爽和温暖的季节，分别（被认为是好或更好）。随着森林覆盖率和流域面积的增加，模型的模拟效果在暖季和冷季都逐渐下降。这表明，开发的模型更好地工作在城市化流域。此外，SWAT和SWAT校准不确定度程序（SWAT-CUP）程序分别运行每个站的流量预测精度的混合方法SWAT比较。在校准过程中，只有31%的站点和34%的验证运行的ENASH值>= 0.50。这项研究表明，耦合人工神经网络与半分布式模型可以导致改进的日径流预测无资料流域。(C)2015 Elsevier B. V.版权所有。
To improve daily flow prediction in unmonitored watersheds a hybrid model was developed by combining a quasi-distributed watershed model and artificial neural network (ANN). Daily streamflow data from 29 nearby watersheds in and around the city of Atlanta, Southeastern United States, with leave-one site -out jackknifing technique were used to build the flow predictive models during warm and cool seasons. Daily streamflow was first simulated with the Soil and Water Assessment Tool (SWAT) and then the SWAT simulated baseflow and stormflow were used as inputs to ANN. Out of the total 29 test watersheds, 62% and 83% of them had Nash-Sutcliffe values above 0.50 during the cool and warm seasons, respectively (considered good or better). As the percent forest cover or the size of test watershed increased, the performances of the models gradually decreased during both warm and cool seasons. This indicates that the developed models work better in urbanized watersheds. In addition, SWAT and SWAT Calibration Uncertainty Procedure (SWAT-CUP) program were run separately for each station to compare the flow prediction accuracy of the hybrid approach to SWAT. Only 31% of the sites during the calibration and 34% of validation runs had ENAsH values >= 0.50. This study showed that coupling ANN with semi distributed models can lead to improved daily streamflow predictions in ungauged watersheds. (C) 2015 Elsevier B.V. All rights reserved.