线性时变大系统的分散最优控制与镇定

本文提出一种关于线性时变大系统最优控制的二次型性能指标分解方法。据此研究线性时变大系统局部分散最优控制的逆问题．得到大系统关于某二次型性能指标存在最优状态反馈控制律的条件，同时它也是大系统局部分散镇定的一个新的充分条件．     

基于时间序列的鄱阳湖水位分析与预测

以鄱阳湖外洲水文站为研究对象,主要介绍了水位与水流量的曲线模型以及自回归模型,选择短周期的水位和水流量数据,采用Matlab拟合最优的水位与水流量的曲线模型,剔除水流量对水位的影响,采用自回归模型并基于最小二乘原理对其剩余量进行拟合及预测。结果显示,曲线模型+自回归模型的拟合和预测效果明显优于单一的曲线模型和自回归模型,用后验差法检验显示预测精度等级为好,因此,该模型可对鄱阳湖外洲站水位进行短期预报。     

A nonlinear time series analysis using two‐stage genetic algorithms for streamflow forecasting

Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non‐steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two‐stage genetic algorithms were used to construct nonlinear time series models of 10‐day streamflows of the Wu‐Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications. Copyright © 2008 John Wiley & Sons, Ltd.     

Prediction of hydrological drought durations based on Markov chains: case of the Canadian prairies

Abstract

Hydrological drought durations (lengths) in the Canadian prairies were modelled using the standardized hydrological index (SHI) sequences derived from the streamflow series at annual, monthly and weekly time scales. The rivers chosen for the study present high levels of persistence (as indicated by values exceeding 0.95 for lag-1 autocorrelation in weekly SHI sequences), because they encompass large catchment areas (2210–119 000 km²) and traverse, or originate in, lakes. For such rivers, Markov chain models were found to be simple and efficient tools for predicting the drought duration (year, month, or week) based on annual, monthly and weekly SHI sequences. The prediction of drought durations was accomplished at threshold levels corresponding to median flow (Q50) (drought probability, q?=?0.5) to Q95 (drought probability, q?=?0.05) exceedence levels in the SHI sequences. The first-order Markov chain or the random model was found to be acceptable for the prediction of annual drought lengths, based on the Hazen plotting position formula for exceedence probability, because of the small sample size of annual streamflows. On monthly and weekly time scales, the second-order Markov chain model was found to be satisfactory using the Weibull plotting position formula for exceedence probability. The crucial element in modelling drought lengths is the reliable estimation of parameters (conditional probabilities) of the first- and second-order persistence, which were estimated using the notions implicit in the discrete autoregressive moving average class of models. The variance of drought durations is of particular significance, because it plays a crucial role in the accurate estimation of persistence parameters. Although, the counting method of the estimation of persistence parameters was found to be unsatisfactory, it proved useful in setting the initial values and also in subsequent adjustment of the variance-based estimates of persistence parameters. At low threshold levels corresponding to q < 0.20, even the first-order Markov chain can be construed as a satisfactory model for predicting drought durations based on monthly and weekly SHI sequences.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Sharma, T.C. and Panu, U.S., 2012. Prediction of hydrological drought durations based on Markov chains in the Canadian prairies. Hydrological Sciences Journal, 57 (4), 705–722.     

Prediction of the impact of climate change on drought: an evaluation of six UK catchments using two stochastic approaches

Under future climate scenarios, possible changes of drought patterns pose new challenges for water resources management. For quantifying and qualifying drought characteristics in the UK, the drought severity indices of six catchments are investigated and modelled by two stochastic methods: autoregressive integrated moving average (ARIMA) models and the generalized linear model (GLM) approach. From the ARIMA models, autocorrelation structures are first identified for the drought index series, and the unexplained variance of the series is used to establish empirical relationships between drought and climate variables. Based on the ARIMA results, mean sea level pressure and possibly the North Atlantic Oscillation index are found to be significant climate variables for seasonal drought forecasting. Using the GLM approach, occurrences and amounts of rainfall are simulated with conditioning on climate variables. From the GLM‐simulated rainfall for the 1980s and 2080s, the probabilistic characteristics of the drought severity are derived and assessed. Results indicate that the drought pattern in the 2080s is less certain than for the 1961–1990 period, based on the Shannon entropy, but that droughts are expected to be more clustered and intermittent. The 10th and 50th quantiles of drought are likely higher in the 2080s scenarios, but there is no evidence showing the changes in the 90th quantile extreme droughts. Copyright © 2012 John Wiley & Sons, Ltd.     

Site-specific prediction of West Nile virus mosquito abundance in Greater Toronto Area using generalized linear mixed models

Mosquito surveillance programs provide a primary means of understanding mosquito vector population dynamics for the risk assessment of human exposure to West Nile virus (WNv). The lack of spatial coverage and missing observations in mosquito surveillance data often challenge our efforts to predict this vector-borne disease and implement control measures. We developed a WNv mosquito abundance prediction model in which local meteorological and environmental data were synthesized with entomological data in a generalized linear mixed modeling framework. The discrete nature of mosquito surveillance data is accommodated by a Poisson distributional assumption, and the site-specific random effects of the generalized linear mixed model (GLMM) capture any fluctuation unexplained by a general trend. The proposed Poisson GLMMs efficiently account for the nested structure of mosquito surveillance data and incorporate the temporal correlation between observations obtained at each trap by a first-order autoregressive model. In the case study, Bayesian inference of the proposed models is illustrated using a subset of mosquito surveillance data in the Greater Toronto Area. The relevance of the proposed GLMM tailored to WNv mosquito surveillance data is highlighted by the comparison of model performance in the presence of inevitable but quantifiable uncertainties.     

基于空域最小二乘法求解GOCE卫星重力场的模拟研究

本文论述了最小二乘过程中有色噪声的处理方法,提出使用AR模型对GOCE梯度观测值中的有色噪声进行时域滤波,数值模拟结果验证了该方法的有效性。利用数值模拟验证了直接求逆方法和PCCG法求解大型法方程的有效性,后者的效率远远高于前者。联合加入噪声（有色噪声和白噪声）的卫星重力梯度张量径向分量观测值Vzz和SST观测值,分别使用空域最小二乘法和SA方法恢复了180阶全球重力场模型,前者求解重力场模型的大地水准面和重力异常在180阶次的精度分别为3.01cm和0.75mGal,优于SA方法求解模型的精度。     

钟差预报AR模型异常值探测的Bayes方法

主要研究了时间序列AR模型的异常值探测问题。首先在一定的限制条件下,将AR模型的异常值探测问题转化为线性回归模型的异常值探测问题;并在正态-Gamma先验条件下,计算了基于均值漂移模型和方差膨胀模型异常值事件发生的后验概率。然后运用Bayes方法对异常值进行了估算。最后通过卫星钟差实测数据计算,比较了模型修正前后预报的情况,验证了新方法的有效性。     

FORECASTING THE MJO INDEX BASED ON SSA-AR

Experiments of forecasting daily bi-variate index of the tropical atmospheric Madden-Julian Oscillation (MJO) are performed in the context of adaptive filtering prediction models by combining the singular spectrum analysis (SSA) with the autoregressive (AR) methods.the MJO index,a pair of empirical orthogonal function (EOF) time series,called RMM1 and RMM2,predicts by the combined statistical SSA and AR models:firstly,according to the index of historic observation decomposed by SSA and then reconstructed by selecting the first several components based on prominent variance contributions;after that,established an AR prediction model from the composite (scheme A) or built the forecast models for each of these selected reconstructed components,separately (Scheme B).Several experimental MJO index forecasts are performed based on the models.The results show that both models have useful skills of the MJO index forecast beyond two weeks.In some cases,the correlation coefficient between the observed and predicted index series stays above 0.5 in 20 leading days.The SSA-AR model,based on the reconstructed composite series,has better performance on MJO forecast than the AR model,especially for the leading time longer than 5 days.Therefore,if we build a real-time forecast system by the SSA-AR model,it might provide an applicable tool for the operational prediction of the MJO index.     

Bianchi II with time varying constants. Self-similar approach

We study a perfect fluid Bianchi II models with time varying constants under the self-similarity approach. In the first of the studied model, we consider that only vary G and Λ. The obtained solution is more general that the obtained one for the classical solution since it is valid for an equation of state ω∈(−1,∞) while in the classical solution ω∈(−1/3,1). Taking into account the current observations, we conclude that G must be a growing time function while Λ is a positive decreasing function. In the second of the studied models we consider a variable speed of light (VSL). We obtain a similar solution as in the first model arriving to the conclusions that c must be a growing time function if Λ is a positive decreasing function.