| 引用本文: | 张晓斌,李抗彬,郝改瑞,等.基于BP神经网络的新安江模型初始土壤蓄水量计算研究[J].灌溉排水学报,2021,(3):15-22. |
| ZHANG Xiaobin,LI Kangbin,HAO Gairui,et al.基于BP神经网络的新安江模型初始土壤蓄水量计算研究[J].灌溉排水学报,2021,(3):15-22. |
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| 摘要: |
| 【目的】克服传统经验折减系数法在计算新安江模型初始土壤蓄水量方面的缺点，并提高新安江模型在湿润半湿润地区的应用效果。【方法】结合流域初始土壤蓄水量的影响因素和神经网络模型特点，提出构建基于BP神经网络的新安江模型初始土壤蓄水量计算方法。【结果】在3种输入因子组合方式下，当BP神经网络隐含层节点大于11时，模拟训练期模型应用效果达到项目精度评价指标的甲等水平，预测检验期的9个样本，均有6个以上样本检验合格；当BP神经网络隐含层节点数从4个变化到21个时，模型评价指标纳什效率系数从0.51变到0.97、均方根误差从11.77降到2.74；与采用传统经验折减系数法计算新安江模型初始土壤蓄水量相比，采用BP神经网络模型应用效果明显占优，且能克服经验折减系数法计算土壤初始蓄水量需要选择流域一场暴雨或久旱未雨才能开始计算和计算过程数据不能中断的缺点。【结论】在湿润半湿润地区采用BP神经网络模型计算新安江模型初始土壤蓄水量具有可行性和适用性；当神经网络输入因子和隐含层节点数选择合理时，模型模拟和预测精度较高。 |
| 关键词: 初始土壤蓄水量；BP神经网络；新安江模型；径流模拟 |
| DOI：10.13522/j.cnki.ggps.2020324 |
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| Using BP Network to Estimate Initial Soil Water Storage in Xin’anjiang Model |
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ZHANG Xiaobin, LI Kangbin, HAO Gairui, ZHANG Xiaopeng
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1. Yuncheng University, Yuncheng 044000, China; 2. Xi’an Land Water and Electricity Measurement and Control Co.Ltd, Xi’an 710043, China; 3. Xi’an University of Technology, Xi’an 710048, China
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| Abstract: |
| 【Objective】Xin’anjiang model is a hydrological model widely used for catchment modelling, but it application needs to know the initial soil moisture storage. Such initial soil moistures were traditionally estimated using the empirical reduction coefficient method which has some shortcomings, and this paper aims to present an alternative method to improve the estimate of this initial soil moisture storage when applying the model to humid and semi humid areas.【Method】The proposed method is based on the BP neural network and calculates the initial soil moisture storage using some easy-to-measure factors that are thought to affect moisture distribution in soil.【Result】Using three input factors, when the number of nodes in the hidden layer was more than 11, the accuracy index of the BP network model reached first-class level during the training stage. Of the nine samples used in the test of the model, six met the required criterion. It was also found that when the number of the nodes in the hidden layer varied between 4 and 21, the Nash–Sutcliffe efficiency coefficient in the model evaluation increased from 0.51 to 0.97, with the associated root mean square errors decreasing from 11.77 to 2.74. Compared with the traditional empirical reduction coefficient method, the BP neural network model is superior in resolving the constraints in the former, including that it needs a rainstorm or a long drought to start the calculation and that the calculation needs to be continuous in time.【Conclusion】The BP neural network model proposed in this paper is feasible to calculate the initial soil water storage when applying the Xin’anjiang model to humid and semi humid areas. It can accurately estimate the initial soil water storage in a catchment if the number of input factors and the nodes in the hidden layers are rationally selected. |
| Key words: initial soil water storage; BP neural network; Xin’anjiang model; runoff simulation |
