| 基于BP人工神经网络的长江河口地区土壤盐分动态模拟及预测 |
| |
| 引用本文： | 余世鹏,杨劲松,刘广明,邹 平.基于BP人工神经网络的长江河口地区土壤盐分动态模拟及预测[J].土壤,2008,40(6):976-979. |
| |
| 作者姓名： | 余世鹏 杨劲松 刘广明 邹 平 |
| |
| 作者单位： | 1. 中国科学院南京土壤研究所,南京,210008
2. 中国科学院南京土壤研究所,南京,210008;河海大学水文水资源与水利工程科学国家重点实验室,南京,210098 |
| |
| 基金项目： | 中国科学院知识创新工程项目，国家高技术研究发展计划(863计划)，国务院三峡建设委员会办公室重点项目，国家重点实验室基金，国家自然科学基金，江苏省自然科学基金 |
| |
| 摘 要： | 为开展长江河口地区土壤盐分动态的中长期模拟与预测,采用人工神经网络中应用较为成熟和广泛的BP网络建立长江河口地区土壤盐分与降雨量、蒸发量、长江水电导率、内河水电导率、地下水位、地下水电导率6因子间的非线性神经网络响应模型。网络模型结构为6-11-1,隐含层单元数用"试错法"确定。选择合适的参数训练和学习网络模型后,对河口地区2003年各月平均根层土壤电导率进行预测,并与线性回归模型预测结果进行比较。结果表明:BP网络模型较线性回归模型具有更高的预测精度,平均相对预测误差为7.3%,预测值与实测值相关性良好,可以满足实际应用需求。
|
| 关 键 词： | BP人工神经网络 长江河口 土壤盐分动态 预测 |
Simulation and prediction of soil salt dynamics in the Yangtze River estuary with BP artificial neural network |
| |
| YU Shi-peng,YANG Jin-song,LIU Guang-ming,ZOU Ping.Simulation and prediction of soil salt dynamics in the Yangtze River estuary with BP artificial neural network[J].Soils,2008,40(6):976-979. |
| |
| Authors: | YU Shi-peng YANG Jin-song LIU Guang-ming ZOU Ping |
| |
| Institution: | Institute of Soil Science, Chinese Academy of Sciences |
| |
| Abstract: | In order to conduct a medium-long term simulation and prediction of the soil salt dynamics in the Yangtze River estuary, a nonlinear artificial neural network response-model among 6 factors of soil salt, rainfall, evaporation, river water EC, freshwater EC, water table and groundwater EC was established with the BP network which has been applied maturely and widely in all kinds of artificial neural networks. The structure of the network model was fixed on 6-11-1. The cells number of the hidden layer was confirmed using the Trial-and-Error method. After the network model was trained and tested by choosing appropriate parameters, it was applied to predict the average root-layer soil EC in the Yangtze River estuary in every month of 2003. The predicted result from the network model was compared with that from the linear regression model. Results showed that the BP network model had higher precision in predicting the salt dynamics than the linear regression model. The average relative error was 7.3% and the correlativity between predicted value and actual value was all right, both of which showed that the simulation and prediction of the BP artificial neural network could meet the need of the practical application. |
| |
| Keywords: | Back-propagation artificial neural network Yangtze River estuary Soil salt dynamics Prediction |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《土壤》浏览原始摘要信息 |
| 点击此处可从《土壤》下载免费的PDF全文 |
|
