沈阳市生活垃圾排放现状及产生量预测

随着城市化进程加快、人口数量快速增加及人民生活水平普遍提高,城市生活垃圾管理已成为世界各国,特别是发展中国家城市面临的主要挑战.作为中国东北最大的中心城市,沈阳正面临城市生活垃圾产生量急剧增加的现状.文章通过对沈阳市城市生活垃圾排放现状的分析,发现2002年后沈阳市生活垃圾产生量和清运量逐年增加,生活垃圾成分变化显著....     

基于LS-SVR、BP-ANN和MLR模型的PM10浓度预测

利用宁东能源化工基地PM10和气象监测数据,分别采用LS-SVR、BP-ANN和传统MLR模型预测PM10浓度变化。结果表明,较BP-ANN模型、MLR模型,LS-SVR模型能更好地刻画PM10浓度与各气象因素间的非线性相依关系,更准确地预测PM10浓度。     

Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

基于遗传算法的支持向量机预测有机物自燃点的研究

根据定量构效关系(QSPR)原理,研究自燃点(AIT)与其分子结构间的内在定量关系。以265种有机化合物作为样本集,随机选择238种作为训练集,27种作为测试集,用遗传算法(GA)进行变量选择,分别建立多元线性回归(MLR)模型和支持向量机(SVM)模型研究有机物的自燃点与其分子结构间的关系。通过分析,发现造成模型预测效果不佳的原因是试验数据本身存在问题。通过对2个模型的比较,结果为GA-SVM模型明显优于GA-MLR模型,说明自燃点与其分子结构间具有很强的非线性关系。     

海河流域社会经济发展对河流水质的影响

海河流域社会经济快速发展,主要河流水质恶化.本文基于海河流域人口规模、经济产值和土地利用变化过程,结合河流社会经济发展指标和水质变化统计分析,从流域废污水排放和水资源利用等角度分析社会经济对水环境影响机制.研究表明,流域人口规模大幅增长、工业生产强度大幅提高,工业聚集区由北京-天津地区扩展到北京-天津-唐山、石家庄、聊城-德州等地区,导致海河流域工业废水和生活污水排放规模迅速上升,成为河流水质恶化直接驱动力.城市扩张是流域土地利用变化最显著特征,近30年来城市用地面积增加85%,北京-天津-唐山城市群规模扩大,造成流域水资源开发利强度加剧,降低河流自净缓冲能力.因子分析表明,流域影响河流水质因素分解为农村、城市和自然等3个方面,其中城市化过程和农村社会经济发展对河流水体污染物浓度水平影响非常显著.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

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.     

异味阈值与分子结构特征关系的研究进展

异味阈值和分子结构间的关系,受到阈值准确识别和嗅觉产生机制研究的制约,是一个非常复杂的问题.本文简要介绍了多元线性回归、拓扑指数、神经网络和分子生物学等几种研究异味阈值和分子结构关系的方法,探讨了目前存在的问题以及未来的发展趋势.     

基于基团贡献法的有机化合物好氧生物降解预测模型研究

从MITI-Ⅰ试验中筛选出587种不同类型有机化合物的可用数据,通过对这些物质的结构进行拆分,随机选择其中50种化合物作为验证集,另外537种作为训练集,利用多元线性回归(MLR)和支持向量机(SVM)2种计算方法分别建立模型。结果表明,芳香酸、醛、芳香碘和叔胺等功能基团对有机化合物的好氧生物降解性影响较大;MLR模型总体预测正确率为81.43%,验证集正确率为82%,SVM模型总体预测正确率为87.90%,验证集正确率为86%。所建立的2种定量结构与生物降解性关系(QSBR)模型有效,可用于化学品的好氧生物降解性评价。