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作 者:耿英豪 周晓栋 漆俊璐 李昂 王崇润 Geng Yinghao;Zhou Xiaodong;Qi Junlu;Li Ang;Wang Chongrun(School of Intelligent Manufacturing and Control Technology,Xi'an Mingde Polytechnic Institute,Xi'an,China;School of Information Engineering,Xi'an Mingde Polytechnic Institute,Xi'an,China;School of Information Engineering,Xi'an Wenli University,Xi'an,China;School of Mechatronic Engineering,Xi'an University of Business and Economics,Xi'an,China;Higher Technical Vocational College,Xi'an Mingde Institute of Technology,Xi'an,China)
机构地区:[1]西安明德理工学院智能制造与控制技术学院,陕西西安 [2]西安明德理工学院信息工程学院,陕西西安 [3]西安文理学院信息工程学院,陕西西安 [4]西安工商学院机电工程学院,陕西西安 [5]西安明德理工学院高等职业技术学院,陕西西安
出 处:《科学技术创新》2025年第4期60-63,共4页Scientific and Technological Innovation
摘 要:本文主要针对黄河水沙监测数据进行了深入研究,利用多元线性回归模型和判别分类模型对黄河的年总水流量、年总排沙量以及水沙通量进行了精准分析。首先,通过多元线性回归模型,本文成功建立了含沙量与时间、水位、水流量之间的定量关系,并利用最小二乘法估算了回归系数,得到了准确的含沙量预测值。其次,通过对水沙通量的计算和分析,本文利用判别分类模型将监测方案分为无波动、小波动、中波动、大波动四种类型,并提出了相应的监测方案建议。This paper conducts an in-depth study on the monitoring data of water and sediment in the Yellow River,using multiple linear regression models and discriminant classification models to analyze the annual total water flow,annual total sediment discharge,and water-sediment flux accurately.Firstly,through the multiple linear regression model,this paper successfully establishes quantitative relationships between sediment concentration and time,water level,and water flow,and estimates regression coefficients using least squares method to obtain accurate sediment concentration predictions.Secondly,by calculating and analyzing the water-sediment flux,this paper classifies monitoring schemes into four types:no fluctuation,small fluctuation,medium fluctuation,and large fluctuation using discriminant classification models,and proposes corresponding monitoring scheme suggestions.Finally,based on the linear regression model,predictions are made for water and sediment regulation strategies and riverbed elevation ten years ahead,providing scientific and effective technical support for Yellow River water and sediment monitoring.
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