摘要
含沙量的输移过程具有确定性的运动规律和不确定性的统计规律,其复杂性导致含沙量过程难以预报。文中遵循数理统计认识事物的思路,描绘水沙运动规律,基于不确定性分析建立由试验系统和估计系统两部分组成的含沙量过程预报模型,在确定性预报模型的基础上利用贝叶斯方法进行不确定性分析,求出预报对象的后验分布作为最终预报值。经黄河潼关站检验期6场洪水过程验证,基于不确定性分析的含沙量过程预报模型检验期6场洪水的确定性系数高于0.7,沙峰相对误差低于20%,输沙总量相对误差低于10%,模型预报精度显著提高,可以为黄河流域生态保护和高质量发展提供技术支持与决策信息。
There are the certain movement rule and the uncertain statistical rule in the sediment transport process,which makes it difficult to forecast the sediment.According to the mathematical statistics and the sediment motion statistics,a model was established for forecasting the sediment based on uncertainty analysis,which consists of the experimental system and the estimating system.Bayesian method was used for uncertainty analysis based on deterministic forecast model,then the Bayesian posterior distribution of the forecast factors was taken as the final forecast value.Checked by the observed data of Tongguan station,the results of the uncertainty forecasting model show that the certainty coefficients are higher than 0.7,the relative errors of sediment peak are lower than 20%,and the relative errors of total sediment transport are lower than 10%.The forecast accuracy of the model has been significantly improved,which can provide technical support and decision-making information for ecological protection and high-quality development of the Yellow River Basin.
作者
李楠
张振华
张雨
宋扬
张倩
LI Nan;ZHANG Zhenhua;ZHANG Yu;SONG Yang;ZHANG Qian(Dezhou Water Conservancy Bureau of Shandong,Dezhou 253004,China)
出处
《干旱区资源与环境》
CSSCI
CSCD
北大核心
2022年第9期141-148,共8页
Journal of Arid Land Resources and Environment
关键词
不确定性
含沙量预报
贝叶斯预报
响应函数模型
神经网络模型
uncertainty
sediment forecasting
Bayesian forecasting system
response function
artificial neural networks
作者简介
李楠(1982-),女,汉族,辽宁抚顺人,硕士研究生,高级工程师,主要从事水文学及水资源、泥沙运动力学等方面的研究。E-mail:cherub0725@foxmail.com;通讯作者:张振华(1982-),男,汉族,山东滨州人,硕士研究生,高级工程师,主要从事水文学及水资源方面的研究。E-mail:zhan⁃gzhenhua2710@126.com
