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投稿时间：2006-01-16
投稿时间：2006-01-16
中文摘要: 建立了一种考虑前期影响雨量和采用人工神经网络的非线性扰动模型。模型结构与NLPM-API模型相似， 不同之处在于采用人工神经网络模拟输入扰动项与输出扰动项之间的相互关系。 采用牧马河和鲇鱼山水库流域的日降雨径流资料对模型进行了率定和校核。 结果表明， 所建模型与线性扰动模型、NLPM-AMN模型和NLPM-API模型相比， 两个流域在率定期的模型效率系数增长幅度分别为10.84%, 1.54%, 10.6%和21.59%, 0.67%, 10.11%；在检验期的模型效率系数增长幅度分别为5.56%, 0.
中文关键词: 水文预报 线性扰动模型 人工神经网络 前期影响雨量 NLPM-AMN模型
Abstract:A nonlinear perturbation model (NLPM) based on Artificial Neural Network (ANN) and considering the antecedent precipitation index (API) is proposed and developed. The model structure is similar to the NLPM-API model. The difference is that the ANN is adopted to simulate the relationship between the input perturbing terms and the output perturbing terms. The daily rainfall-runoff data from the Mumahe and Nianyushan reservoir basins is selected to test the model. The proposed model is compared with the LPM, NLPM-AMN and NLPM-API models, the model efficiencies in these two basins are increased 10.84%, 1.54%, 10.6% and 21.59%, 0.67%,10.11% during calibration period; 5.56%, 0.97%, 4.41% and 11.86%, 1.76%, 7.97% during verification period, respectively. All other assessment indexes are also superior to other models.
keywords: flood forecasting linear perturbation model artificial neural network antecedent precipitation index NLPM-AMN model
文章编号：20070104 中图分类号： 文献标志码：
基金项目:教育部重点科学技术支持项目资助（104204）
| 作者 | 单位 |
| 庞博 | 武汉大学 水资源与水电工程科学国家重点实验室，湖北 武汉 430072 |
| 郭生练 | 武汉大学 水资源与水电工程科学国家重点实验室，湖北 武汉 430072 |
| 林凯荣 | 武汉大学 水资源与水电工程科学国家重点实验室，湖北 武汉 430072 |
作者简介:
引用文本：
庞博,郭生练,林凯荣.考虑前期影响雨量的NLPM-AMN模型[J].工程科学与技术,2007,39(1):18-22.
.A Nonlinear Perturbation Model Based on Artificial Neural Network and Considering the Antecedent Precipitation Index[J].Advanced Engineering Sciences,2007,39(1):18-22.
引用文本：
庞博,郭生练,林凯荣.考虑前期影响雨量的NLPM-AMN模型[J].工程科学与技术,2007,39(1):18-22.
.A Nonlinear Perturbation Model Based on Artificial Neural Network and Considering the Antecedent Precipitation Index[J].Advanced Engineering Sciences,2007,39(1):18-22.