An interval type-2 fuzzy stochastic approach for regional-scale electric power system under parameter uncertainty

In this study, an interval type-2 fuzzy stochastic linear programming method (IT2FSLP) is developed to support regional-scale electric power system (REM) planning. The IT2FSLP-REM model is based on an integration of interval type-2 fuzzy sets boundary programming and stochastic linear programming techniques enable it to have robust abilities to the deal with uncertainties expressed as type-2 fuzzy intervals and probabilistic distributions within a general optimization framework. Moreover, it can reflect dynamic decisions for energy supply and energy conversion processes, as well as provide capacity expansion options with multiple periods. The developed model is applied to a case of planning regional-scale energy and environmental systems to demonstrate its applicability. Based on a two-step solution algorithm, reasonable solutions have been obtained, which reflect tradeoffs among economic cost, environmental requirements, and energy-supply security. Thus, the lower and upper solutions of IT2FSLP-REM would then help energy authorities adjust or justify allocation patterns of regional energy resources and services.     

1982~2010年洞庭湖流域植被指数的变化及其与气候因子的相关分析

基于GIMMS 3 g、PAL、LTDR V3、FASIR及MODIS 5种不同的遥感影像数据,及洞庭湖流域30个气象站点的气温、降水和日照时数月值数据,采用逐像元一元线性回归模型,对比分析了在不同数据集背景下的植被变化趋势,并基于时间序列长度以及数据精度的考虑,选择GIMMS 3 g作为研究洞庭湖流域植被覆盖变化的基础数据集,进而利用相关系数和多元线性回归分析模型探讨流域植被覆盖变化与降水、气温、日照时数的关系。结果表明:(1)过去29 a间,流域GIMMS 3 g NDVI在时空尺度上均以增加趋势为主,1998~2000年出现最大的降低,以植被覆盖较好的山区减少最快;(2)去除年际变化趋势和季节性影响的NDVI与同期降水量距平、累积3个月气温距平值及同期日照时数距平值相关性程度最高;(3)统计意义上,降水量、气温和日照时数解释洞庭湖流域植被月NDVI变化的37%,日照时数对该流域NDVI变化的影响最大,其次为降水和气温;(4)在时间和空间范畴,生长季NDVI可以作为反映洞庭湖流域森林覆盖率变化的指标。     

Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R² = 0.80. For SEM, Chl a and eight variables were used: NH₄-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu.     

Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.In this study, five different modelling techniques are compared and evaluated, i.e. multiple linear regression (MLR), classification and regression trees (CART), boosted regression trees (BRT), generalized additive models (GAM), and artificial neural networks (ANN). Each method is used to model site index of homogeneous stands of three important tree species of the Taurus Mountains (Turkey): Pinus brutia, Pinus nigra and Cedrus libani. Site index is related to soil, vegetation and topographical variables, which are available for 167 sample plots covering all important environmental gradients in the research area. The five techniques are compared in a multi-criteria decision analysis in which different model performance measures, ecological interpretability and user-friendliness are considered as criteria.When combining these criteria, in most cases GAM is found to outperform all other techniques for modelling site index for the three species. BRT is a good alternative in case the ecological interpretability of the technique is of higher importance. When user-friendliness is more important MLR and CART are the preferred alternatives. Despite its good predictive performance, ANN is penalized for its complex, non-transparent models and big training effort.     

针对大尺度区域的大气环境容量综合估算方法

针对大尺度区域的特点,提出了将箱模型法、模拟法与线性规划法相结合的大气环境容量估算方法.其基本思路是将大尺度区域划分为一系列小区域,将真实污染源简化为源单元,基于线性规划法寻求区域最大容量及其优化配置方案.采用箱模型以保证源单元尺度的环境质量要求,采用模拟法以考虑二次污染对前体物环境容量的制约,将箱模型容量与模拟法容量作为规划法的约束条件.新方法使3种传统方法相互取长补短,其显著特征表现为:兼顾了污染源的局地性和区域性的影响;兼顾了污染物的一次影响和二次影响;大气环境容量的规划布局具有区域优化特征.应用新方法对我国东南沿海某区域进行了大气环境容量的评估试验,结果表明该方法合理可行.     

大鹏湾环境容量研究Ⅱ:环境容量规划

采用基于响应场的线性规划方法,考虑非点源与湾外污染物输入形成的本底值,结合深圳沿岸排污口的分布计算了大鹏湾的环境容量,并给出了总量分配方案.结果表明,大鹏湾物理自净能力差,但水体容量大,具有较大的稀释容量,加上污染物的生化降解,大鹏湾具有较大的环境容量.BOD5、总氮、总磷的最大允许排放总量分别为79.0,11.418,0.86t/d,BOD5、总氮、总磷的剩余环境容量分别为61.34,9.318,0.43t/d.     

基于GLM模糊评价在煤矿安全中的应用

煤矿安全评价是现代绿色矿山建设的主要指标之一。为对煤矿安全进行有效的评价,综合分析影响煤矿安全的人员因素、地质因素、技术设备因素、环境因素等各项指标。应用广义线性理论和SAS统计分析方法,建立基于GLM模糊评价模型。通过计算各项指标对应等级的煤矿安全影响权重〖AKω- ij,结合模糊综合评价方法得出煤矿的安全生产状况,同时通过优势比分析各指标的敏感性因子,得出防范指标危险性的有效措施。研究表明:该模型依据标准整理数据,操作性强,较准确地得出煤矿安全的状况,为煤矿的安全评价和管理决策提供依据。     

碳数和官能团对直链易挥发化合物异味阈值的影响规律

以具有代表性的异味特征-异味阈值为研究对象,采用三点比较式臭袋法测定了醇类、醛类、酮类、酸类和硫醇类5个系列常见异味化合物的异味阈值,研究了异味阈值与碳链长度之间的关系,并以官能团疏水性参数量化表征官能团结构,研究了异味阈值与官能团类型之间的关系.研究发现,分子碳链长度和官能团类型都能影响异味阈值的大小,其中异味阈值与碳链长度关系为y=axb或lgy=cx+d(y:异味阈值,x:碳原子数);异味阈值与官能团疏水常数π参数关系为y=aebx或lgy=cx+d(y:异味阈值,x:官能团疏水性参数).所得模型能定量描述碳链长度和官能团类型对异味阈值的影响,为异味机理的进一步探索提供了有力支撑.此外,对异味阈值与上述两因素的多元线性拟合及残差分析结果表明,碳链长度和官能团类型能够影响化合物异味阈值,但二者并非异味阈值的完全决定因素,研究决定异味阈值的全面结构因素,需大量后续工作.     

Differences in the rainwater composition on two consecutive days

The relationship between the rainwater composition on two consecutive rain days was analyzed. Logrono, a remote station in the North of Spain was chosen for the analyses. The concentration of the major ions in the rainwater of the first rain day is linearly related to the concentration of the second rain day except for those ions whose source is the soil (Ca²⁺ and Mg²⁺). These ions are related to SO₄ ^2? and NO₃ ^? in the first rain day, but not in the second.