基于地理加权回归模型的葡萄园土壤砷含量高光谱反演

以吐鲁番盆地葡萄园土壤中砷(As)为研究对象,分析15种光谱变换下的土壤光谱反射率数据与土壤As含量的相关性,构建土壤As含量预测的偏最小二乘回归(PLSR)模型和地理加权回归(GWR)模型.结果表明:葡萄园土壤原始光谱率(R)经一阶微分( FD)、平方根一阶微分(SRFD)、平方根二阶微分( SRSD)、倒数二阶微分...     

GM（1,1）模型简化算法及其在噪声影响预测中的应用

对ＧＭ（１，１）模型经差分形式化及令定写成直线方程形式。据最小二乘法原理，原方程的参数辨识可借助线性回归方法来实现。从而使ＧＭ（１，１）模型的应用更显普及和实用化。     

区域PM2.5时空回归建模与预测

基于区域PM_(2.5)时空建模和预测的需要及PM_(2.5)浓度呈现明显的时空分布趋势的状况,以苏南地区2014年PM_(2.5)日监测数据为实验数据,使用回归克里格对区域PM_(2.5)进行时空建模和估值。利用最小二乘法建立了PM_(2.5)与时空位置的三元二次回归趋势模型,建模点趋势值与实测值间的平均误差接近于0,表明趋势模型拟合效果较好;拟合了样点残差的理论变异函数模型,表明该地区PM_(2.5)的空间和时间相关性范围分别为150 km和4 d;基于该模型,使用时空普通克里格对残差进行时空插值;插值结果与趋势项相加,得到PM_(2.5)回归克里格估值结果;通过对比不考虑趋势的时空普通克里格估值结果,发现考虑时空趋势的时空回归克里格法精度提高了1. 29%。对所提方法进行了创新性分析,并对不足之处进行了讨论。     

校准曲线回归计算方法的比较

本文对目前国内所使用和已提出讨论的校准曲线回归计算方法与国际标准校准曲线回归计算方法进行了比较和讨论,表明无论采用何种回归方法进行计算,对测定结果的精密度和准确度均无显著影响。但为了使校准曲线的回归计算方法国际化、标准化,建议采用国际标准回归法。     

许昌市浅层地下水中离子总量与电导率间关系的研究

通过实验,用回归分析法,研究了许昌市浅层地下水中离子总量与电导率间的定量关系,可用电导法快速了解水质中的离子总量和衡量水质全分析中主要被测离子总和的质量.     

监测数据的线性回归处理方法

回归分析方法在环境监测中应用广泛,当进行两种监测方法现场对比试验时,考虑到两种监测方法都有误差,宜采用正交回归法及综合回归法.本文阐明了三种回方法的原理、应用范围及计算,并给出计算程序,可供监测人员使用.     

紫外光诱导荧光分析仪结合多元线性回归算法在城市河流常规污染指标监测中的应用

为建立一种针对城市河流水体常规污染指标的快速原位监测方法,首次运用紫外光诱导荧光分析仪对扬州市60条城市河流进行水体三维荧光光谱(EEM)测量,形成了具有多样性的水质样本集合.利用峰值拾取法、相关性分析和主成分分析3种方式从三维荧光光谱中提取溶解性有机物(DOM)污染信息,结合多元线性回归算法(MLR),建立与化学需氧...     

线性回归分析方法的不确定度计算

化学分析方法如何进行不确定度计算,并以其为指标控制检测质量,似乎还没有满意的统一方法.本文根据对最常用的线性回归方法的分析,拟从这方面进行探讨.1 回归模型不确定度带宽参数的计算回归处理后得到的是一种平均化的数学模型.尽管平均值并不能完全消除随机误差,显然比之原始测量数据的不确定度带要窄得多.对于(?)=a+bx这样的模型,当x=(?)时,(?)=(?),此点(叫模型的重心点)的不确定度仅取决于(?)的误差,其标准偏差应为     

基于偏最小二乘的絮体图像信息与水质的相关性

针对污水处理运行过程COD、BOD5、TN和TP实时测量的问题,提出一种基于絮体形态参数的偏最小二乘法模型.采用图像分析技术对污泥絮体形态特征进行提取,基于相关性从提取的形态参数和试验期间的运行参数中选择模型的输入变量,通过偏最小二乘法建立输入变量与4个水质指标的预测关系模型.结果表明,COD、BOD5、TN、TP的交...     

多条回归曲线合并的简单方法探讨

回归曲线在化验分析工作中的应用颇为广泛。在实际运用中,回归曲线的合并问题是我们经常遇到的。两条回归曲线的合并方法已非常成熟,至于三条以上回归曲线的合并方法,据现有资料报道,采用的都是逐条曲线合并的方法,不仅计算过程非常复杂,而且用何种合并顺序尚无定论。本文用一种新思路,提出一种与合并顺序无关的多条回归曲线合并的简单方法,供同仁参考。1合并的思路与方法鉴于逐条合并的种种缺点,我们从两条曲线合并公式中得到启迪,寻找一个简单易懂又可将多条曲线同时合并的方法。两条回归曲线合并公式[1]如下：从以上公式中可以…     

基于随机森林与神经网络的高光谱土壤重金属Zn含量反演

在河北省保定市白洋淀区域采集115个土壤样品进行重金属含量分析和室内光谱测量,分别将BP神经网络、随机森林、决策树、多元线性回归、K近邻回归、AdaBoost回归和偏最小二乘回归法应用于全部原始波谱数据和基于双层随机森林选择后的波段数据。结果表明,基于原始波谱数据的土壤重金属Zn元素含量的反演模型精度较低,而通过双层随机森林选择出光谱数据中与土壤重金属Zn信息相关的波段,减轻了网络模型的过拟合问题,提高了模型预测精度;与其他模型比较,结合双层随机森林和BP神经网络构建的反演模型对研究区土壤重金属Zn含量预测效果最佳。     

Advantages of Geographically Weighted Regression for Modeling Benthic Substrate in Two Greater Yellowstone Ecosystem Streams

Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m² cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r ². For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r ² values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.     

PLS and PCR Methods in the Assessment of Coastal Water Quality

Partial least squares regression analysis (PLS) and principal component regression analysis (PCR) were examined asmethodological procedures for assessing the quality of coastalwaters in a tourist area. Four variables related to the trophicstate of waters, namely nitrate, nitrite, ammonia and phosphate were analyzed. The models resulting from PLS and PCR were verysimilar. Both defined three groups of water masses characterizedby different nutrient loadings. These groups were in accordancewith those obtained by numerical classification. The PLS methodwas selected as the optimal model, on the basis of its lowerprediction errors (lower Press and Rmsd values). For managementpurposes, this statistical model allows mesotrophic conditions,reflecting some nutrient enrichment over background conditions,to be characterized and the successful diagnosis of additionalsamples within this context.     

Simultaneous spectrophotometric determination of trace amount of polycyclic aromatic hydrocarbons in water samples after magnetic solid-phase extraction by using projection pursuit regression

Magnetic solid-phase extraction based on coated nano-magnets Fe₃O₄ was applied for the preconcentration of four polycyclic aromatic hydrocarbons (PAHs; anthracene, phenanthrene, fluorine, and pyrene) in environmental water samples prior to simultaneous spectrophotometric determination using multivariate calibration method. Magnetic nanoparticles, carrying target metals, were easily separated from the aqueous solution by applying an external magnetic field so, no filtration or centrifugation was necessary. After elution of the adsorbed PAHs, the concentration of PAHs was determined spectrophotometrically with the aid of a new and efficient multivariate spectral analysis base on principal component analysis-projection pursuit regression, without separation of analytes. The obtained results revealed that using projection pursuit regression as a flexible modeling approach improves the predictive quality of the developed models compared with partial least squares and least squares support vector machine methods. The method was used to determine four PAHs in environmental water samples.     

Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R ² and lower corrected Akaike information criterion (AIC_C). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R ² and lower AIC_C. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.     

Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: data-driven methods for variable selection

Statistical models of microbial water quality inform risk management for water recreation. Current research focuses on resource-intensive, location-specific data collection and water quality modeling, but this approach may be cost-prohibitive for risk managers responsible for numerous recreation sites. As an alternative, we tested the ability of two data-driven models, tree regression and random forests with conditional inference trees, to select readily available hydrometeorological variables for use in linear mixed effects (LME) models predicting bacterial density. The study included the Chicago Area Waterway System (CAWS) and Lake Michigan beaches and harbors in Chicago, Illinois, at which Escherichia coli and enterococci were measured seasonally in 2007–2009. Tree regression node variables reduced data dimensionality by >50 %. Variable importance ranks from random forests were used in a forward-step selection based on R ² and root mean squared prediction error (RMSPE). We found two to three variables explained bacteria densities well relative to random forests with all variables. LME models with tree- or forest-selected variables performed reasonably well (0.335?<?R ²?<?0.658). LME models for Lake Michigan had good prediction accuracy with respect to the single sample maximum standard (72–77 %), but limited sensitivity (23–62 %). Results suggest that our alternative approach is feasible and performs similarly to more resource-intensive approaches.     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.     

污水处理厂还原硫化物和臭气浓度的电子鼻监测技术探究

用电子鼻监测技术探究污水处理厂还原硫化物质量浓度和臭气质量浓度预测方法。结果表明,使用响应面分析法(RSM)建立还原硫化物质量浓度与电子鼻响应值关系,构建还原硫化物质量浓度预测模型,准确率可达95%。使用偏最小二乘法(PLS)建立不同质量浓度还原硫化物的传感器响应值与对应臭气质量浓度之间的关系,构建臭气浓度预测模型,并用实际样品验证。     

Statistically Extracted Fundamental Watershed Variables for Estimating the Loads of Total Nitrogen in Small Streams

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km²) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.     

Environmetrics to evaluate marine environment quality

The environmetric data analysis of analytical datasets from sediment and benthic organisms samples collected from different sampling sites along the coast of Black Sea near to City of Varna, Bulgaria has given some important indications about the bioindication properties of both type of samples. Various multivariate statistical methods like cluster analysis, principal components analysis, source apportioning modeling and partial least square (PLS) modeling were used in order to classify and interpret the parameters describing the chemical content of the coastal sediments (major components, heavy metals and total organic carbon) and benthic organisms (heavy metals). It has been shown that seriously polluted coastal zones are indicated in the same way by all benthic species, although some specificity could be detected for moderate polluted regions' e.g. polychaeta accumulated preferably Co, Cr, Cu, and Pb; crustacea - As, Cd, and Ni; mollusca - Zn. The identified latent factors responsible for the dataset structure are clearly indicated and apportioned with respect to their contribution to the total mass or total concentration of the species in the samples. The linear regression and PLS models indicated that a reliable forecast about the relation between naturally occurring chemical components and polluting species accumulated in the benthic organisms is possible.