Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.     

非线性回归法计算曝气设备清水氧传递系数

分析对比作图法、线性回归法和非线性回归法计算曝气设备清水氧传递系数(KLa)各自的优缺点。指出在实际测定曝气设备性能中,使用非线性回归法计算KLa更加可靠．而且可以使测试过程更加容易控制。所以在实际测试过程中,推荐使用非线性回归法计算KLa。     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

基于数字图像比色法测定水中氟化物

在酸性溶液中,氟能破坏二甲酚橙〖CD*2〗锆显色体系,使溶液颜色从紫红色变为橙色或黄色,通过采集图像颜色信息构建3个特征颜色分量的多元线性回归模型,用以定量测定水中氟。该方法在0 mg/L~1.5 mg/L范围内线性良好,方法检出限为0.03 mg/L。两个质量浓度水平的氟化物标准溶液6次测定结果的RSD分别为1.4%和1.3%,相对误差分别为0.3%和1.7%。     

安全投入各要素分配方向的研究

针对灰色关联分析法存在的分辨系数的选择主观性较大,用关联系数的平均值得到的关联度降低了评价准确性等缺点,提出用经济学中的偏相关分析和不变替代弹性(CES)生产函数分析方法,建立安全投入与事故函数分析模型。应用SPSS和EViews软件,找出安全投入和事故之间偏相关系数绝对值最大的要素和标准化回归系数绝对值最大的要素,作为优先投入方向,并按照系数绝对值大小进行排序,为以后企业进行安全投入提供理论参考,避免企业资源的浪费。这两种方法可操作性强,主观随意性小,得到的结果可信性高。     

中国交通运输碳排放强度时空交互特征及跃迁机制

采取多种空间分析的方法,对2002～2020年交通运输业碳排放强度的时空交互特征进行剖析,通过得到的时空跃迁类型与面板分位数模型进行嵌套来探究其跃迁机制,最后根据不同的跃迁机制引入地理探测器模型来考察影响交通运输业碳排放强度的不同因素之间的交互作用效应.结果表明: ①中国30个省区的交通运输业碳排放强度整体呈波动下降态势,在空间上的聚集水平也相对稳定.②ESTDA的时空交互特征表明,西北地区和周边邻接空间单位的关系不稳定,变化和波动较大.而东部沿海城市等经济发达地区已经形成了成熟的交通运输网络,因此局部空间格局也相对稳定,但仍有部分地区存在时空竞争性.③交通运输业碳排放强度时空跃迁可分为4类驱动或制约模式（人口-经济-城镇化制约模式;人口-经济-城镇化-设施制约模式;技术-消费-产业驱动模式和技术-产业-规制驱动模式）.大部分省份受低分位制约和高分位驱动两种模式的影响,仅有少部分省份受高分位制约和低分位驱动的作用影响,且绝大多数属于西北或西南地区.④根据得出的交通运输业碳排放强度跃迁机制进一步引入地理探测器模型,注重多因子的协调发展,加强区域间协同治理.     

基于统计回归模型的硫化矿石自燃倾向性鉴定指标研究

合理的提出硫化矿石自燃倾向性的鉴定指标,准确鉴别其自燃倾向性,对于矿山防火工作有着重要的意义。本文采用应用非常广泛的一类随机模型—统计回归模型,首先通过分析硫化矿石自燃氧化机理,找到影响硫化矿石自燃倾向性的3个主要影响因素,实验获得相关数据;其次基于3个因素的数据,通过统计分析,建立回归模型,对硫化矿石自燃倾向性进行预测。     

安全投入模型的研究

从安全投入产出分析的角度为安全投入决策提出一个新视角,相比于直接用生产函数等数学方法得到安全投入与安全产出的关系,通过软件分析得出各个要素与安全产出的关系,再进行拟合,最后得到安全产出回归方程,这样得到的分析结果更准确和可信。通过实例可以证明如果要确定安全投入要素之间的分配比例可以采用生产函数分析方法,如要预测安全产出可以采用根据散点图做出的回归方程。另外,在分析安全投入要素相对重要度时,因为各个投入要素的数量级和单位的不同,回归方程系数应选择标准化回归系数;要预测安全投入产生的安全产出则应采用非标准化回归系数。     

地震人员伤亡快速评估模型研究

每年地震都会给社会造成巨大人员伤亡,如果震后能迅速预知地震人员伤亡数量,这对挽救生命损失是十分有意义的。统计了近10年来中国大陆数10次强震、中强震灾害损失,根据影响伤亡的主要因素,用线性回归分析法,建立地震人员伤亡快速评估模型,并对模型进行验证,使之可用于地震预警。     

长江三角洲地区冬季能见度特征及影响因子分析

利用Micaps提供的2013和2014年冬季长江三角洲地区(以下简称长三角)28个站点的地面常规观测资料、NCEP FNL再分析资料和国家环境保护部发布的PM_2.5质量浓度自动检测数据,分析了长三角冬季大气能见度特征,以及空气污染物和气象条件对能见度的影响.2013年冬季长三角霾天发生频率为53.4%.多元非线性回归分析表明,PM_2.5质量浓度、地表10m风速、500~850hPa水平风垂直切变、相对湿度、925~1000hPa垂直温差、850~925hPa假相当位温差这6个因子能够解释能见度变化的81.6%.气象条件对能见度的作用与污染物浓度相当,热力因子的贡献大约是动力因子的2倍.PM_2.5质量浓度越低,空气质量越好,以及相对湿度大于70%时,相对湿度通过气溶胶吸湿增长对能见度的作用越强.考虑PM_2.5质量浓度的影响时,相对湿度对能见度的贡献提高了1倍.利用2014年冬季资料验证多元拟合方程,效果较好.