我国赤潮的预测与控制研究现状

近年来,我国沿海地区赤潮越来越频繁,对水域环境、海水养殖业和人体健康等造成了很大的威胁。本文结合我国现阶段赤潮的研究现状,在分析赤潮的危害和发生条件的基础上,提出了赤潮的预测方法及相应的防治对策。     

Modified grey model for estimating traffic tunnel air quality

This study compared three forecasting models based on the mean absolute percentage errors (MAPE) of their accuracy in forecasting air pollution in a traffic tunnel: the Grey model (GM), the combination model used four sample point and five sample point prediction with GM (1,1)(GM(1,1)^4 + 5), and the modified grey model (MGM). An MGM was combined using the four points of the original sequence using the original grey prediction GM (1,1) for short-term forecasting. The proposed method cannot only enhance the prediction accuracy of the original grey model, but can also solve the jump data forecasting problem something for which the original grey model is inappropriate. The MAPE was applied to the models, and the MGM found the proposed method to be simple and efficient. The MAPE of MGM, calculated over 3 h of forecasts, were as follows: 10.12 (Upwind), 10.07 (Middle) and 7.68 (Downwind) for CO; 10.79 (Upwind), 6.05 (Middle) and 5.98 (Downwind) for NO _x , and 11.67 (Upwind), 7.32 (Middle) and 4.56 (Downwind) for NMHC. The MGM model results reveal that the combined forecasts can significantly decrease the overall forecasting error. Results of this demonstrate that MGM can accurately forecast air pollution in the Kaohsiung Chung–Cheng Tunnel.     

Grey Box and Component Models to Forecast Ozone Episodes: A Comparison Study

For the purpose of short-term forecasting of high ozone concentration episodes stochastic models have been suggested and developed in the literature. The present paper compares the quality of forecasts produced by a grey box and a component time-series model. The summer ozone patterns for three European urban areas (two continental and one mediterranean) are processed. By means of forecast performance indices according to EC and WHO guidelines, the following features of the models could be found: The grey box model is highly adaptive and produces forecasts with low error variance that increases with the time horizon of forecast. The component model is more 'stiff' that results in a higher forecast-error variance and poorer adaption in detail. The forecast horizon, however, could be enlarged with this model. The accuracy of predicting threshold exceedance is similar for both models. This can be understood from the assumption of a cyclical time development of ozone that was made for both models.     

A proposed impact assessment method for genetically modified plants (AS-GMP Method)

An essential step in the development of products based on biotechnology is an assessment of their potential economic impacts and safety, including an evaluation of the potential impact of transgenic crops and practices related to their cultivation on the environment and human or animal health. The purpose of this paper is to provide an assessment method to evaluate the impact of biotechnologies that uses quantifiable parameters and allows a comparative analysis between conventional technology and technologies using GMOs. This paper introduces a method to perform an impact analysis associated with the commercial release and use of genetically modified plants, the Assessment System GMP Method. The assessment is performed through indicators that are arranged according to their dimension criterion likewise: environmental, economic, social, capability and institutional approach. To perform an accurate evaluation of the GMP specific indicators related to genetic modification are grouped in common fields: genetic insert features, GM plant features, gene flow, food/feed field, introduction of the GMP, unexpected occurrences and specific indicators. The novelty is the possibility to include specific parameters to the biotechnology under assessment. In this case by case analysis the factors of moderation and the indexes are parameterized to perform an available assessment.     

“十二五”及2020年电力需求预测研究

准确地把握未来10年的电力需求走势是做好电力规划、安排电力建设的重要基础.考虑到不同部门电力需求具有不同的决定因素和机制,本文把全社会用电量分为居民生活用电、农业用电、工业用电、建筑业用电和服务业用电等五个部分,构建、拟合不同的电力需求方程,预测电力需求.预测的主要结果是:“十二五”期间全社会用电量年均增长率约为7.8%,2015年用电量超过6万亿kWh,“十三五”期间的年均增长率为6.1%,2020年用电量接近8.2万亿kWh.未来10年,单位产值用电量将逐步下降,电力消费增长率将低于GDP增长率,电力需求弹性分别为0.84和0.72.电力需求结构将发生显著变化,到2020年居民生活用电占全社会用电比重为20%左右,工业用电比重将从当前的73%下降到2020年的63%.     

长江上游月降水人工神经网络预测模型

长江上游月降水量预测对于三峡库区及整个长江流域水资源管理具有重要意义。根据长江上游不同气候区降水差异,选取玉树、九龙和宜宾3个代表性气象站点近60 a的月降水量数据,运用反向传播神经网络、径向基函数神经网络、广义回归神经网络和多元线性回归法,确定降水时滞和降水月份,建立月降水预测模型,来预测未来一个月的降水量,并采用均方误差和判定系数来验证和对比各种模型的模拟效果。结果显示：人工神经网络模型总体上优于多元线性回归,特别是反向传播神经网络的模拟结果各站表现较好,在确定合理的输入变量和网络结构后,可以尝试作为长江上游各站月降水预测模型。〖     

基于广义加权组合预测法的建设用地需求预测——以苏州市为例

对现行采用的多种常用建设用地需求量预测方法进行了比较,并以苏州市为例,采用5种预测模型进行了对比分析,利用近13年的统计数据进行了验证.结果表明,广义加权组合预测法得到的结果误差最小、精度最高.     

A Simple Framework for Incorporating Seasonal Streamflow Forecasts into Existing Water Resource Management Practices1

Gong, Gavin, Lucien Wang, Laura Condon, Alastair Shearman, and Upmanu Lall, 2010. A Simple Framework for Incorporating Seasonal Streamflow Forecasts Into Existing Water Resource Management Practices. Journal of the American Water Resources Association (JAWRA) 46(3):574-585. DOI: 10.1111/j.1752-1688.2010.00435.x Abstract: Climate-based streamflow forecasting, coupled with an adaptive reservoir operation policy, can potentially improve decisions by water suppliers and watershed stakeholders. However, water suppliers are often wary of straying too far from their current management practices, and prefer forecasts that can be incorporated into existing system modeling tools. This paper presents a simple framework for utilizing streamflow forecasts that works within an existing management structure. Climate predictors are used to develop seasonal inflow forecasts. These are used to specify operating rules that connect to the probability of future (end of season) reservoir states, rather than to the current storage, as is done now. By considering both current storage and anticipated inflow, the likelihood of meeting management goals can be improved. The upper Delaware River Basin in the northeastern United States is used to demonstrate the basic idea. Physically plausible climate-based forecasts of March-April reservoir inflow are developed. Existing simulation tools and rule curves for the system are used to convert the inflow forecasts to reservoir level forecasts. Operating policies are revised during the forecast period to release less water during forecasts of low reservoir level. Hindcast simulations demonstrate reductions of 1.6% in the number of drought emergency days, which is a key performance measure. Forecasts with different levels of skill are examined to explore their utility.     

Uncertainty Reduction of the Flood Stage Forecasting Using Neural Networks Model1

Abstract: The Elman Discrete Recurrent Neural Networks Model (EDRNNM), which is one of the special types of neural networks model, is developed and applied for the flood stage forecasting at the Musung station (No. 1) of the Wi‐stream catchment, which is one of the International Hydrological Program representative basins, Korea. A total of 135 different training patterns, which involve hidden nodes, standardization process, data length, and lead‐time, are selected for the minimization of the architectural uncertainty. The model parameters, such as optimal connection weights and biases, are estimated during the training performance of the EDRNNM, and we apply them to evaluate the validation performance of the EDRNNM. Sensitivity analysis is used to reduce the uncertainty of input data information of the EDRNNM. As the results of sensitivity analysis, the Improved EDRNNM consists of four input nodes resulting from the exclusion of Dongkok station (No.5) in initial five input nodes group of the EDRNNM. The accuracy of flood stage forecasting during the training and validation performances of the Improved EDRNNM remains the same as that of the EDRNNM. The Improved EDRNNM, therefore, gives highly reliable flood stage forecasting. The best optimal EDRNNM, so called the Improved EDRNNM, is determined by elimination of the uncertainties of architectural and input data information in this study. Consequently, we can avoid unnecessary data collection and operate the flood stage forecasting system economically.     

香港的空气污染指数预报

介绍了香港空气污染指数预报的方法和所取得的良好社会效果。通过香港空气污染指数预报的学习结合内地环境保护工作的情况，谈了几点体会。