Relationships between vitamin A and PCBs in grey seal mothers and pups during lactation

A previous study has shown a simultaneous increase of vitamin A and PCBs in grey seal (Halichoerus grypus) milk at late lactation (Debier et al., 2004). Here we sought to understand this unexpected relationship by comparing the dynamics of vitamin A and PCBs in the different tissue compartments of transfer. Lactating grey seals and their pups were sampled longitudinally in Scotland during the 2006 breeding season. As blubber reserves decreased, concentrations of vitamin A and PCBs increased during lactation in the inner layer of maternal blubber. A concomitant rise was observed in milk and consequently in the serum of suckling pups. The similar dynamics of vitamin A and PCBs in milk and inner blubber suggest a common mechanism of mobilisation from maternal body stores and transfer into the milk. A panel data analysis highlighted a negative impact of PCBs in milk and pup serum on vitamin A status in pup serum.     

非时序加权灰色关联分析在环境质量综合评价中的应用

本文根据灰色关联分析的基本性质，提出一种环境质量综合评价的新方法。该方法用待评价样本各指标值组成参考数列，环境质量分级标准值组成被比较数列，通过计算参考数列与被比较数列的关联度，按最优关联识别原则综合评价环境质量等级。用４个实例与其它方法进行了对比，取得满意的效果。     

REAL-TIME SNOW SIMULATION MODEL FOR THE MONONGAHELA RIVER BASIN1

ABSTRACT: The Pittsburgh District, U.S. Army Corps of Engineers, is responsible for operating two multipurpose reservoirs in the 7384 square mile (19198 square kilometer) Monongahela Basin. A third reservoir, presently under construction, will soon be operating. The real-time forecasting of runoff for operational purposes requires simulation of snow accumulation and snowmelt throughout the Basin during the winter season. This article describes capabilities of SNOSIM, a model being developed for performing such simulation. The application of this model as part of a comprehensive system of water control software, and some initial simulation results are presented.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

灰色系统方法在城市生活垃圾量预测中的应用

利用灰色关联分析方法分析了影响城市生活垃圾量的因素，建立了生活垃圾量的ＧＭ（１，１）预测模型，预测了未来的城市生活垃圾量。该法具有较高的精度，简便，可行。     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

PROBABILISTIC GREAT LAKES HYDROLOGY OUTLOOKS1

ABSTRACT: The Great Lakes Environmental Research Laboratory developed a semiautomatic software package for making hydrological outlooks for the Great Lakes. These include basin moisture storages, basin runoff, lake heat storage, lake evaporation, heat fluxes, and net lake supplies, one or more full months into the future. The package combines GLERL's rainfall-runoff and lake evaporation models with near real-time data reduction techniques to represent current system states. Users select historical meteorologic record segments as candidate future scenarios to generate deterministic near real-time hydrological outlooks. GLERL has extended the package to make probabilistic outlooks for a decision-maker who must estimate the risk associated with his decisions. GLERL matches National Weather Service meteorologic outlook probabilities by selecting groups of historical meteorologic sequences, and constructs embedded outlook intervals for each hydrologic variable of interest. Interval probabilities are assigned from comparisons over a recent evaluation period. This physically-based approach for generating outlooks offers the ability, as compared to other statistically-based approaches, to incorporate improvements in the understanding, of process dynamics as they occur in the future and to respond reasonably to conditions initial to a forecast (such as heat and moisture storages), not observed in the past.     

关于铁路行车事故预测的应用研究

本文论述了事故预测对铁路行车安全的重要性,并应用灰色预测模型和概率回归估计法模型研制了预测软件,进行铁路行车事故的宏观及微观预测,为采取有针对性的防范措施提供一个可靠信息。