| 基于人工神经网络预测弯道段冰塞壅水 |
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| 引用本文： | 王军,伊明昆,付辉,尹运基,高月霞.基于人工神经网络预测弯道段冰塞壅水[J].冰川冻土,2006,28(5):782-786. |
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| 作者姓名： | 王军 伊明昆 付辉 尹运基 高月霞 |
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| 作者单位： | 合肥工业大学,土木建筑工程学院,安徽,合肥,230009;机械工业第六设计研究院,河南,郑州,450003;同济大学,上海,200092 |
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| 基金项目： | 国家自然科学基金项目(10372028)资助 |
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| 摘 要： | 寒冷地区河流冬季常形成凌洪灾害,冰情的预报和预测大致可分为冰塞出现的位置、上游水位升高和开河冰塞的预报.依据实验资料,分析了弯槽段冰塞壅高上游水位和佛汝得数以及冰流量之间的变化关系,采用BP网络对水位壅高及弯槽断面水位进行了模拟预测并和回归分析的结果进行了对比,结果表明:人工神经网络技术可以提高对水位的模拟预测精度.
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| 关 键 词： | 冰塞水位 冰流量 人工神经网络 回归分析 |
| 文章编号： | 1000-0240(2006)05-0782-05 |
| 收稿时间： | 01 13 2006 12:00AM |
| 修稿时间： | 04 4 2006 12:00AM |
Application of Artificial Neural Network to Predict the Increase in Stage due to Ice Jam in a Bend |
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| WANG Jun,YI Ming-kun,FU Hui,YIN Yun-ji,GAO Yue-xia.Application of Artificial Neural Network to Predict the Increase in Stage due to Ice Jam in a Bend[J].Journal of Glaciology and Geocryology,2006,28(5):782-786. |
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| Authors: | WANG Jun YI Ming-kun FU Hui YIN Yun-ji GAO Yue-xia |
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| Institution: | 1. School of Civil Engineering, Hefei University of Technology, Hefei Anhui 230009, China ; 2. Sixth Institute of Mechanics, Zheng zhou Henan 450003 China ; 3. Tongji University, Shanghai 200092, China |
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| Abstract: | Severe flooding or ice-related damage can result from a rise in stage associated with ice jams in cold regions.It is important to predict the stage or the evolution of an ice jam in a river for disaster mitigation.Prediction of ice-related phenomena is correlative with ice jam location,rise in upstream water level and the information of breakup ice jam.There are empirical models,threshold models,statistical models,regression models,logistic regression,discriminant function analysis and artificial intelligence.Recently,the research focus on the possibilities of occurrence of ice jams in a straight channel.Based on experiment data,the variety of stage in a curved channel in upstream is analyzed in this paper.In addition,water levels in the upstream and at many cross sections in a bend channel are studied with artificial neural network.The results show that the artificial neural network method is more precise than the regression models. |
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| Keywords: | increase in stage associated with ice jam ice discharge artificial neural network Regression analysis |
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