摘要
针对变形预报的不确定性,以MCMC算法和贝叶斯预测理论为基础,提出了变形概率预报方法,该方法以概率分布的形式描述变形预报的不确定性,通过概率规则实现预报的递推过程。利用宁杭高速路基沉降数据进行数值计算,定量分析了预报值及其可靠性区间等信息,并与最小二乘估计、免疫算法的预报结果进行比较,结果表明了该方法的有效性和可行性。
A probabilistic prediction method of deformation is proposed based on the MCMC algorithm and the Bayesian Prediction Theory.This method describes the uncertainty of deformation prediction using probability distributions and implement the recursive process of prediction by probability rules.The settlement data from the Nanjing-Hangzhou high-speed roadbed is used to quantitatively analyze the forecast values,reliability intervals and so on,and then the results are compared with those obtained by the least squares estimation and the immune algorithm,and it has shown that the proposed method is effective and feasible.
出处
《测绘学报》
EI
CSCD
北大核心
2017年第4期526-532,共7页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(41364001)
甘肃省自然科学基金(1508RJEA065)
兰州交通大学科技支撑计划(ZC2014002)~~
关键词
预报不确定性
测量误差
参数不确定性
GIBBS抽样
概率预报
prediction uncertainty
measurement error
parameter uncertainty
Gibbs sampling
probability predication