| 新安江产流模型与改进的BP汇流模型耦合应用 |
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| 引用本文： | 阚光远,刘志雨,李致家,姚成,周赛.新安江产流模型与改进的BP汇流模型耦合应用[J].水科学进展,2012,23(1):21-28. |
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| 作者姓名： | 阚光远 刘志雨 李致家 姚成 周赛 |
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| 作者单位： | 1.河海大学水文水资源学院, 江苏南京 210098; |
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| 基金项目： | 国家重点基础研究发展计划(973)资助项目(2010CB951101);高等学校博士学科点专项科研基金资助项目(20090094110005)~~ |
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| 摘 要： | 为提高新安江模型的汇流计算精度并减少经验因素对参数率定的影响,将新安江产流模型与改进的BP汇流模型相耦合,建立XBK(XAJ-BP-KNN)模型。该模型以前期模拟流量和新安江产流模型计算的产流量作为BP网络的输入,出口断面流量作为网络输出,拟合汇流的非线性关系,代替新安江模型的分水源、线性水库及河道马斯京根法的汇流计算;采用相似原理和K-最近邻算法,基于历史样本的模拟误差及相应影响要素对网络输出进行误差修正,实现了无前期实测流量的连续模拟;模型使用SCE-UA算法与遗传早停止LM算法相结合的全局优化方法进行参数优选。在呈村流域的验证表明XBK模型的模拟精度高于新安江模型,全局优化方法能找到最优参数,降低了模型的使用难度。
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| 关 键 词： | 新安江模型 人工神经网络 反向传播算法 K-最近邻算法 SCE-UA算法 |
| 收稿时间： | 2011-03-23 |
Coupling Xinanjiang runoff generation model with improved BP flow concentration model |
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| KAN Guang-yuan
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LIU Zhi-yu
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LI Zhi-jia
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YAO Cheng
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ZHOU Sai.Coupling Xinanjiang runoff generation model with improved BP flow concentration model[J].Advances in Water Science,2012,23(1):21-28. |
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| Authors: | KAN Guang-yuan LIU Zhi-yu LI Zhi-jia YAO Cheng ZHOU Sai |
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| Institution: | 1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China;2.National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Nanjing 210098, China;3.Hydrology Bureau Ministry of Water Resources, Beijing 100053, China |
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| Abstract: | In order to improve the flow concentration accuracy of the Xinanjiang model and to reduce the influence of personal experiences on the model calibration,a new rainfall-runoff model called XBK(XAJ-BP-KNN) is developed,coupling the Xinanjiang runoff generation model with the improved version of the back propagation(BP) flow concentration model.The latter uses the BP neural network algorithm to simulate the nonlinear relationship of the flow concentration process.The flow calculated by Xinanjiang runoff model and antecedent flow are used as the XBK inputs to a BP simulation network.The flow inputs are routed by the BP concentration model to the outlet of the network,which forms the hydrograph at the outlet of the BP simulation network.XBK uses the similarity theory and the K-nearest neighbor algorithm for pattern recognition in an effort to correct the simulation error due to the absence of the observed initial flow data.XBK parameters are optimized globally using the combined method of the shuffled complex evolution(SCE-UA) algorithm and the genetic early stopping Levenberg-Marquardt(LM) algorithm.The XBK model is applied to the Chengcun watershed.Compared to the original version of the Xinanjiang model,the result shows that a better model simulation can be achieved with XBK.XBK is easy to apply,and the combined global optimization algorithm is able to identify optimal parameter values. |
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| Keywords: | Xinanjiang model artificial neural network back propagation algorithm K-nearest neighbor algorithm SCE-UA algorithm |
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