本文已被：浏览 7154次 下载 1969次
中文摘要: 为寻求较为合理的径流量预测方法，利用GA-BP组合模型进行径流量预测的研究，首先利用GA算法(Genetic algorithm)全局搜索能力定位出一个较好的搜索空间，再利用BP算法(Back propagation)神经网络的局部寻优能力，以陕西省北洛河状头站实测数据为基础进行径流量预测。结果表明：利用GA-BP组合模型预测2001—2010年结果的相对误差分别为-8.9%、-12.6%、-7.4%、9.4%、-7.6%、15.0%、-5.5%、10.7%、13.3%和-8.2%，预测精度较高，从相对误差和训练次数比较可以看出，GA-BP组合模型优于基本BP模型和GA模型。
Abstract:The paper has developed a GA-BP combination model to forecast the runoff, in order to seek more reasonable runoff forecasting methods. First, use the GA global search capability to locate a better search space, then use the local optimization ability of BP neural network. This forecast mod
el was used the measured data of Zhuangtou Hydrometric Station in Beiluo River from 1960 to 2010. The results showed that: The relative error of the predicted results from 2001 to 2010 were -8.9%、-12.6%、-7.4%、9.4%、-7.6%、15.0%、-5.5%、10.7%、13.3% and -8.2%, respectively. The predicted results of GA-BP combination model were better than the basic BP model and the GA model from the relative err
or and the training numbers.
文章编号： 中图分类号： 文献标志码：
基金项目:国家自然科学基金项目(51079120)；国家自然科学基金项目(50979088)
| Author Name | Affiliation |
| WANG Guang-yu1, XIE Jian-cang1, ZHANG Jian-long2 |
引用文本：
