Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups,(i) training dataset and(ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages,distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

基于连续比例Logistic回归模型的贝叶斯判别分析

针对传统贝叶斯判别分析方法处理实际问题的局限性,提出一种基于连续比例Logistic回归模型的贝叶斯判别分析方法.首先基于连续比例Logistic回归模型建立半参数密度比模型,通过经验似然法估计模型的参数,并使用贝叶斯定理计算后验概率进行分类预测.然后对比新方法与传统方法的回判正确率,统计模拟表明当总体数据符合正态分布时,2者判别能力相当,否则,提出的新方法能够更好地判别不同的数据特征.最后运用新方法分析真实的数据集,验证了新方法在分类预测中的准确性和稳健性,与传统方法相比,更适用于实际应用中多元分类问题的建模和预测.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

基于二元Logistic回归模型分析机器人辅助子宫内膜癌术后并发症危险因素

目的:探索基于二元Logistics回归模型分析机器人辅助子宫内膜癌患者术后并发症的危险因素。方法:回顾性分析2021年5月—2022年5月在空军军医大学第一附属医院收治的96例子宫内膜癌患者的临床资料,根据手术方式不同将所有患者分为常规组(n=47)和机器人组(n=49),并比较两组患者并发症发生率。同时,经二元Logistic回归模型分析影响术后并发症的危险因素。结果:机器人组术后并发症发生率低于常规组(6.12%Vs 31.91%,P<0.05)。经二元Logistic回归模型分析,年龄>60岁、BMI>24 kg/m^(2)、术中出血量>200 ml、有贫血史、常规手术是影响患者术后出现并发症的独立危险因素(P<0.05)。利用Bootstrap法内验证,预测模型AUC为0.818,特异性71.8%,灵敏性77.8%,95%CI 0.720~0.917。结论:影响机器人辅助子宫内膜癌术后并发症的主要因素为贫血史、手术时间、BMI、年龄、手术方式,针对合并此类危险因素的患者需采用相应的干预措施,从而降低患者术后并发症,改善预后。

基于Logistic回归和神经网络的甘肃省道路结冰预警模型研究

为更好地开展公路交通道路结冰预报预警服务工作,利用甘肃省道路结冰高发区路段(甘肃武威以东)的交通气象站逐小时观测资料,分析道路结冰空间分布特征,探讨道路结冰与气象要素的相关性,采用Logistic回归法和神经网络算法构建道路结冰预警模型。结果表明:甘肃省道路结冰主要集中在冬季(12月至次年2月),其中00:00—10:00和22:00—23:00(北京时)出现道路结冰的频率较高。Logistic回归模型和神经网络模型对未发生结冰事件的预测准确率较高,分别为91.9%和96.2%;针对发生结冰事件,Logistic回归模型的预测准确率较低,为31.6%,而神经网络模型的预测准确率可达44.6%,说明2种模型对道路结冰预警有一定指示意义,神经网络模型预测效果优于Logistic回归模型。

Establishment and verification of a surgical prognostic model for cervical spinal cord injury without radiological abnormality

Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prognostic model of cervical spinal cord injury without radiological abnormality. This retrospective analysis included 43 patients with cervical spinal cord injury without radiological abnormality. Seven potential factors were assessed: age, sex, external force strength causing damage, duration of disease, degree of cervical spinal stenosis, Japanese Orthopaedic Association score, and physiological cervical curvature. A model was established using multiple binary logistic regression analysis. The model was evaluated by concordant profiling and the area under the receiver operating characteristic curve. Bootstrapping was used for internal validation. The prognostic model was as follows: logit(P) =-25.4545 + 21.2576 VALUE + 1.2160SCORE-3.4224 TIME, where VALUE refers to the Pavlov ratio indicating the extent of cervical spinal stenosis, SCORE refers to the Japanese Orthopaedic Association score(0–17) after the operation, and TIME refers to the disease duration(from injury to operation). The area under the receiver operating characteristic curve for all patients was 0.8941(95% confidence interval, 0.7930–0.9952). Three factors assessed in the predictive model were associated with patient outcomes: a great extent of cervical stenosis, a poor preoperative neurological status, and a long disease duration. These three factors could worsen patient outcomes. Moreover, the disease prognosis was considered good when logit(P) ≥-2.5105. Overall, the model displayed a certain clinical value. This study was approved by the Biomedical Ethics Committee of the Second Affiliated Hospital of Xi'an Jiaotong University, China(approval number: 2018063) on May 8, 2018.

A Time-dependent Stochastic Grassland Fire Ignition Probability Model for Hulun Buir Grassland of China

Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.

A Prognostic Model of the Development of Postpartum Purulent-Inflammatory Diseases

Background: Currently, postpartum purulent-inflammatory diseases continue to be a prominent issue in medicine. As a result, numerous scientific publications were devoted to finding the solution to this issue. Primarily these solutions included the idea of optimisation of antibiotic-based disease prevention and therapies. However, the early diagnosis and prognosis of these pathologies were unfortunately overlooked. The Aim of the Study: To build a prognostic model of the development of postpartum purulent-inflammatory diseases. Material and Methods: The main focus of our research was establishment of methods of early diagnosis and prognosis of purulent-inflammatory diseases. The main cohort consisted of 170 women diagnosed with purulent-inflammatory diseases while the control cohort was made of 40 women with an uncomplicated course of pregnancy;patient’s blood serum was analysed using fluorescence spectroscopy. Additionally, we implied a variety of standardised algorithms used during clinical and laboratory examination of the patients with postpartum endometritis. Results: Fluorescence spectra were studied for 40 women of control group and 170 women of the main group. Based on the data obtained using fluorescence spectroscopy and data from clinical and laboratory examinations (extragenital pathology, gynecology-related diseases, risk of miscarriage, surgery, TORCH-infections, colpitis, labour duration > 12 hrs, labour anomalies, maximum blood serum fluorescence spectrum values, fluorescence spectrum ≤ 0.845, age, number of bed days, fetal distress), we have derived a prognostic model of the development of postpartum purulent-inflammatory diseases. Conclusion: As a result, we derived a prognostic model based on the main 13 factors, which contribute to development of postpartum purulent-inflammatory diseases. This model was determined correct with a probability of over 99% (р 2 = 174.74;df = 13).

基于PCA-Logistic回归模型的矿井底板突水危险性研究

为解决煤层底板突水预测难题,提出了基于主成分分析与Logistic回归方法的底板突水预测模型。通过对底板突水危险因素进行分析,选取隔水层厚度、承压水水压、断层落差、断层距工作面距离、煤层采高、煤层倾角6个变量作为研究矿井底板突水的初始影响指标;首先利用主成分分析法对原始指标数据进行降维处理,然后利用建立的Logistic回归模型对数据进行分析预测,最后利用5组待测样本数据对模型进行验证。结果表明:该模型对突水样本的综合预判正确率为90%,利用待测数据进行回判时预测准确率达到80%,说明该预测模型具有一定可靠性,可作为煤矿底板突水预测的一种新方法。

Alternating Direction Method of Multipliers for l_(1)-l_(2)-Regularized Logistic Regression Model

Logistic regression has been proved as a promising method for machine learning,which focuses on the problem of classification.In this paper,we present anl_(1)-l_(2)-regularized logistic regression model,where thel1-norm is responsible for yielding a sparse logistic regression classifier and thel_(2)-norm for keeping betlter classification accuracy.To solve thel_(1)-l_(2)-regularized logistic regression model,we develop an alternating direction method of multipliers with embedding limitedlBroyden-Fletcher-Goldfarb-Shanno(L-BFGS)method.Furthermore,we implement our model for binary classification problems by using real data examples selected from the University of California,Irvine Machines Learning Repository(UCI Repository).We compare our numerical results with those obtained by the well-known LIBSVM and SVM-Light software.The numerical results show that ourl_(1)-l_(2)-regularized logisltic regression model achieves better classification and less CPU Time.

基于二分类Logistic回归模型的太行山丘陵区县域耕地资源潜力估算

耕地红线划定与人-地资源矛盾日益突出背景下,耕地资源潜力的研究与开发日显重要。我国耕地面积近2/3分布在山区,因此山区耕地资源的合理开发利用及其资源潜力的研究尤为重要。本文以华北地区的太行山为研究区域,选择耕地占比和资源潜力最大的丘陵区典型县——河北省井陉县为研究案例,选取13个影响耕地资源潜力的基本生态要素,包括5个地形要素和8个直接气象要素或由气象要素计算得到的间接气象要素,引入二分类Logistic回归分析方法,运用偏最大似然估计向前引入法的拟合方法,筛选提取影响耕地资源潜力的关键生态要素;由模型参数Waldc2统计量分析影响耕地资源潜力的关键生态要素的贡献率排序;由模型参数回归系数β分析耕地资源潜力与生态要素的相关关系;由模型参数发生比率OR分析量化关键生态要素对耕地资源潜力的影响,最终建立Logistic回归模型。基于此模型,在GIS软件中得到井陉县耕地资源潜力分布图,进而估算出县域耕地资源潜力。研究结果表明:13个影响井陉县耕地资源潜力的基本生态要素中8个为关键生态要素;关键生态要素中地形要素配置比气象要素配置更为重要;年平均气温和寒冷指数与耕地资源潜力呈负相关关系,其余生态要素则呈正相关关系;由回归模型估算出井陉县具备垦殖为耕地资源的土地面积为60 400 hm^2,而根据遥感影像解译结果得出的现有耕地资源为45 600 hm^2,由此井陉县尚具有14 800 hm^2的后备耕地资源,相当于现有耕地面积的32.5%,这说明在不考虑垦殖所带来的可能负效应的前提下,井陉县具有较大的后备耕地资源开发潜力,该结论为井陉县后备耕地资源的开发与可持续利用提供了理论依据。

基于GIS和Logistic回归模型的土地利用空间模拟与分析——以龙海市为例

基于2014年龙海市土地利用变更数据、DEM数据和社会经济数据,借助GIS空间分析技术,共设计7个模拟尺度,运用Logistic回归模型选取对试验区有重要贡献的10种驱动因子进行空间统计分析,并对龙海市土地利用空间格局进行模拟.试验结果表明,龙海市模型的最佳模拟尺度为125 m×125 m,在该尺度下耕地、园地、林地、建设用地的空间分布格局模拟精度分别为:82.73%、76.65%、69.52%、88.49%.龙海市土地利用类型与各驱动因子具有显著相关性,高程、人口、可达性因素是决定龙海市土地利用空间格局形成与演变的重要因素,可为研究龙海市未来土地利用动态模拟提供依据.

基于Logistic回归和多指标叠加的短时强降水预报模型

利用2001—2011年中国西北地区东部10个特征站地面常规资料和MICAPS系统特征参数资料,分别运用改进的二元Logistic回归法和综合多指标叠加法,通过短时强降水天气学概念模型识别入型、水汽条件消空、敏感物理参数诊断等方法逐级判别,建立了两种短时强降水预报模型,并运用模型试预报2012年该区域的短时强降水过程。结果表明:两种新建预报模型相比平均气候概率模型试预报效果都有明显提高,而且前者高于后者;其中二元Logistic回归模型试预报TS得分高达46.6%,综合多指标叠加模型试预报TS得分19.6%,而平均气候概率模型试预报TS得分仅9. 7%;除西南气流型两者预报效果相当外,不同概念模型下二元Logistic回归模型试预报效果均优于综合多指标叠加模型。

Logistic回归模型及其在昆虫学中的应用

介绍了应用Logistic回归分析对二值反应的试验数据进行分析的方法 ,以及Logistic回归分析模型参数估计及其统计检验的方法 ,并结合 1个实际例子说明了Logistic回归模型的应用。

非线性混合效应模型拟合Logistic回归在临床试验中的应用

目的探讨非线性混合效应模型拟合Logistic回归在临床试验中的应用。方法采用SAS软件包的NLMIXED过程拟合模型,并以两例药物临床试验资料进行实例分析。结果获得了各参数及其标准误的估计值,并可以对各因素进行直观的解释。结论非线性混合效应模型允许固定效应和随机效应进入模型的非线性部分,可以拟合具有非线性的Logistic回归模型,是临床试验中分析二项分布数据有效方法。

机器学习对H.pylori感染患者的特征变量及预测模型研究

目的 分析H.pylori感染患者感染的危险因素,建立H.pylori感染患者预测模型,为防治H.pylori感染提供参考。方法 选取2021年7至2022年5月在中山市中医院、中山市东凤人民医院、中山市南区医院共1 477例接受H.pylori检测者为研究对象,依据胃镜和~(14)C、~(13)C呼气试验的检测结果,将H.pylori受检人群分为感染组和无感染组,分别进行问卷调查,调查内容包括受检者基本情况、临床表征、慢性基础病、生活和饮食习惯等,共计63个变量。采用单因素及机器学习中的Logistic回归、决策树分析及添加交互项的Logistic回归对H.pylori感染进行多因素分析,并比较3个模型的ROC曲线下的面积、灵敏度、特异度,验证模型的准确性,建立H.pylori感染预测模型,将特征和危险因素建立森林图。结果 Logistic回归分析的AUC为0.7361,灵敏度为0.7615,特异度为0.6034。决策树分析的AUC为0.6528,灵敏度为0.6801,特异度为0.5773。添加交互项后的Logistic回归分析的AUC为0.7388,灵敏度为0.7588,特异度为0.6034。添加交互项的多因素Logistic回归结果显示,有胃胀,口气、口臭,在家煮食午餐,在家无而外出有使用公筷习惯,同居家人有感染,疫情后才使用公筷,居住4~10层楼,同时有胃胀及口气、口臭为模型的显著性变量。结论 胃胀,有口气、口臭,同时有胃胀及口气、口臭,在家煮食午餐,居住的楼层数,外出居家是否使用公筷,是否有使用公筷习惯,家人是否感染H.pylori是感染H.pylori的特征因素,用Logistic回归模型作为主模型进行变量筛选,添加交互后的模型,AUC有所提升,交互项的预测模型对H.pylori感染者预判能力好,运算容易,使用经济、便利,适合区域性推广。

农户参与农耕文化保护和传承意愿研究

农耕文化作为中华文明发展的根基,对文化强国和农业强国建设具有重大意义和关键价值。基于计划行为理论剖析影响农户参与农耕文化保护和传承意愿的因素,以蚌埠市五河县刘朵村为研究区域,通过走访调查收集数据,利用二元Logistic回归模型进行实证分析。研究结果表明:农耕文化的经济价值、社会价值、邻居朋友的带动、基层政府的宣传、参与农耕文化活动的精力和能力以及受教育水平对于农户参与农耕文化保护和传承意愿有显著的正向影响。因此,在推动农耕文化发展中,要切实增强农户的主体意识,加强政府的宣传和推广力度,显现农耕文化的经济价值和社会价值,鼓励农户参与农耕文化发展事业,发挥村干部的示范带头作用,提高农户的自我效能感,调动农户的参与积极性,保障农耕文化稳步有序发展。

条件logistic回归模型配合适度研究

为了检验Pregibon提出的条件logistic配合适度理论的可靠性,本文用Monte Carlo方法对有关统计量进行了统计实验研究,并探讨了有关统计量的应用效果和应用条件。分析了配合适度的影响因素,本文将AIC(赤池信息量准则)统计量引入条件logistic回归模型的配合适度检验中,取得了良好效果,可作为选择“最优”模型的常规统计量。对一个1:2匹配的实例进行了分析。本研究用Fortran77语言自编程序实现了条件logistic模型的参数估计及其检验和评价配合适度的整个计算过程,给实际应用提供了一个有效工具。

基于随机森林模型的城市非法营运车辆识别

区域经济社会的快速发展与交通出行的需求发展不匹配,在一定程度上为非法营运车辆提供了市场契机。城市高速公路的ETC(Electronic Toll Collection)数据可有效稽查高速公路的非法营运车辆,从而优化运行秩序并提升管理水平。文中提取ETC数据的有效字段,采用随机森林算法建立非法营运车辆识别分类器,加入CART(Classification and Regression Tree)分类树模型分类器和二元逻辑回归模型分类器与之对比,并以西南某市高速公路自2022年2月6日~2022年3月8日的ETC指标数据进行实证分析。结果表明,随机森林模型分类器比CART分类树模型分类器和二元逻辑回归模型分类器预测效果更好,其准确性高达98.75%。