城区中有毒气体突发性泄漏时,需要快速对泄漏源进行定位和识别,以便科学预测气体的蔓延及其影响范围。利用基于Bayes推断理论的MCMC(Markov chain Monte Carlo)抽样方法,根据城市中分布的传感器测量信息和气体扩散数值计算模型,构造似...城区中有毒气体突发性泄漏时,需要快速对泄漏源进行定位和识别,以便科学预测气体的蔓延及其影响范围。利用基于Bayes推断理论的MCMC(Markov chain Monte Carlo)抽样方法,根据城市中分布的传感器测量信息和气体扩散数值计算模型,构造似然函数,对泄漏源的位置、强度进行反演。计算了这些参数和空间各点浓度的相关统计量,表明反演结果与泄漏源的真实参数十分吻合。此外,还讨论了传感器测量误差的概率分布对结果的影响。结果表明,误差概率会显著影响计算效果,概率分布越平坦,泄漏源反演信息的不确定度越大。展开更多
为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过...为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。展开更多
Profile likelihood function is introduced to analyze the uncertainty of hydrometeorological extreme inference and the theory of estimating confidence intervals of the key parameters and quantiles of extreme value dist...Profile likelihood function is introduced to analyze the uncertainty of hydrometeorological extreme inference and the theory of estimating confidence intervals of the key parameters and quantiles of extreme value distribution by profile likelihood function is described.GEV(generalized extreme value)distribution and GP(generalized Pareto)distribution are used respectively to fit the annual maximum daily flood discharge sample of the Yichang station in the Yangtze River and the daily rainfall sample in10 big cities including Guangzhou.The parameters of the models are estimated by maximum likelihood method and the fitting results are tested by probability plot,quantile plot,return level plot and density plot.The return levels and confidence intervals of flood and rainstorm in different return periods are calculated by profile likelihood function.The results show that the asymmetry of the profile likelihood function curve increases with the return period,which can reflect the effect of the length of sample series and return periods on confidence interval.As an effective tool for estimating confidence interval of the key parameters and quantiles of extreme value distribution,profile likelihood function can lead to a more accurate result and help to analyze the uncertainty of extreme values of hydrometeorology.展开更多
文摘城区中有毒气体突发性泄漏时,需要快速对泄漏源进行定位和识别,以便科学预测气体的蔓延及其影响范围。利用基于Bayes推断理论的MCMC(Markov chain Monte Carlo)抽样方法,根据城市中分布的传感器测量信息和气体扩散数值计算模型,构造似然函数,对泄漏源的位置、强度进行反演。计算了这些参数和空间各点浓度的相关统计量,表明反演结果与泄漏源的真实参数十分吻合。此外,还讨论了传感器测量误差的概率分布对结果的影响。结果表明,误差概率会显著影响计算效果,概率分布越平坦,泄漏源反演信息的不确定度越大。
文摘为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。
基金supported by the National Basic Research Program of China("973" Program)(Grant Nos.2013CB036406,2010CB951102)the National Natural Science Foundation of China(Grant No.51109224)
文摘Profile likelihood function is introduced to analyze the uncertainty of hydrometeorological extreme inference and the theory of estimating confidence intervals of the key parameters and quantiles of extreme value distribution by profile likelihood function is described.GEV(generalized extreme value)distribution and GP(generalized Pareto)distribution are used respectively to fit the annual maximum daily flood discharge sample of the Yichang station in the Yangtze River and the daily rainfall sample in10 big cities including Guangzhou.The parameters of the models are estimated by maximum likelihood method and the fitting results are tested by probability plot,quantile plot,return level plot and density plot.The return levels and confidence intervals of flood and rainstorm in different return periods are calculated by profile likelihood function.The results show that the asymmetry of the profile likelihood function curve increases with the return period,which can reflect the effect of the length of sample series and return periods on confidence interval.As an effective tool for estimating confidence interval of the key parameters and quantiles of extreme value distribution,profile likelihood function can lead to a more accurate result and help to analyze the uncertainty of extreme values of hydrometeorology.
