GIS based Landslide Susceptibility Mapping of Tevankarai Ar Sub-watershed,Kodaikkanal,India using Binary Logistic Regression Analysis

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.

A comparison of model choice strategies for logistic regression

Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Utilization of Logistical Regression to the Modified Sine-Gordon Model in the MST Experiment

In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.

Residents'satisfaction of Beijing new regulations for domestic waste classification based on binary logistic regression:A case study of Daxing District

On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.

Bio-Demographic Factors Impacting on Employment in Namibia： A Binary Logistic Regression Model.

Despite concerted efforts to create employment opportunities and the realized economic growth between 2000 and 2005, the unemployment rate in Namibia currently stands at 27.4%, according to the Labour Force Survey released in April 2013. The percentage of employed males in Namibia stands at 41.6% while that of employed females stand at 28.8% according to the National Human Resources Plan of May 2013. Analysts have put the blame on adverse climatic conditions, limited levels of skills, access to finance, and the structure of the economy. The frustration and discomfort caused by unemployment, especially among the youth, can threaten the country＇s peace and stability as it negatively impacts on the standard of living, crime rates, family happiness, and drug abuse.To date, studies on employment in Namibia have mainly concentrated on the micro and macro econometric approaches. It is important to examine how bio-demographic characteristics affect employment. This paper uses data from the 2010 Income and expenditure survey to establish the bio-demographic determinants of employment by fitting a binary logistic model. The outcome variable is employment status which is dichotomous. The independent variables which were guided by review of related literature and availability of data in the Income and Expenditure survey data set, included age-group, region, place of residence, marital status, education level, and gender. Results indicated that employment prospects in Namibia were influenced by the region, gender, marital status, and education level.

Factors Associated with Trait Anger Level of Juvenile Offenders in Hubei Province： A Binary Logistic Regression Analysis

The risk factors of high trait anger of juvenile offenders were explored through questionnaire study in a youth correctional facility of Hubei province, China. A total of 1090 juvenile offenders in Hubei province were investigated by self-compiled social-demographic questionnaire, Childhood Trauma Questionnaire（CTQ）, and State-Trait Anger Expression Inventory-Ⅱ（STAXI-Ⅱ）. The risk factors were analyzed by chi-square tests, correlation analysis, and binary logistic regression analysis with SPSS 19.0. A total of 1082 copies of valid questionnaires were collected. High trait anger group（n=316） was defined as those who scored in the upper 27 th percentile of STAXI-Ⅱ trait anger scale（TAS）, and the rest were defined as low trait anger group（n=766）. The risk factors associated with high level of trait anger included： childhood emotional abuse, childhood sexual abuse, step family, frequent drug abuse, and frequent internet using（P〈0.05 or P〈0.01）. Birth sequence, number of sibling, ranking in the family, identity of the main care-taker, the education level of care-taker, educational style of care-taker, family income, relationship between parents, social atmosphere of local area, frequent drinking, and frequent smoking did not predict to high level of trait anger（P〉0.05）. It was suggested that traumatic experience in childhood and unhealthy life style may significantly increase the level of trait anger in adulthood. The risk factors of high trait anger and their effects should be taken into consideration seriously.

Logistic Regression在我国河流水系氮污染研究中的应用

对四川省岷江、沱江及嘉陵江流域和江西省的赣江流域及鄱阳湖地区共 1 70多个水文站的数据进行了相关分析和多元回归分析 .结果表明 ,河流水中硝态氮浓度与年降雨量、人口密度、氮肥施用量、牲畜饲养量、农作物及粮食作物种植面积等因素有较好的相关性 .以以上数据资料为基础 ,将河流水NO3- N的浓度划分为背景浓度 (<0 7mg/L)、受人类活动的显著影响的NO3- N浓度 (>3 0mg/L)以及中间类 (0 7— 3 0mg/L)进行LogisticRegression分析 ,两个Logistic模型的准确度分别达 82 46%和 89 1 9% .运用Logistic模型对整个长江流域河流水中NO3- N浓度进行估计 ,结果与实测值基本相符合 .

用Logistic Regression侦察题目差异功能

题目差异功能 (differentialitemfunctioning,DIF)是构造测验公平性的重要依据 ,DIF的研究与测验的效度有直接的关联。本文通过对DIF的提出作简要的回顾 ,着重介绍如何运用LogisticRegression探测一致性DIF和非一致性DIF ,并例证了学习适应性测验 (AAT)的 6个项目在性别上存在题目差异功能。

基于Binary Logistic模型的棉花出苗情况建模

为了科学、合理地做好盐渍土条件下的棉花出苗预测工作,以便为当地农业生产提供一定借鉴。以新疆巴音郭楞蒙古自治州为例,利用所采集土样中的盐分离子数据以及Binary Logistic模型,模拟不同盐分离子组成时棉花出苗情况。结果表明:当利用Binary Logistic模型对棉花出苗进行模拟时,模型通过了-2对数似然值检验,并且对测试样本有67.9%的模拟准确性。

大学生“慢就业”现象及其影响因素——基于二元Logistic回归模型的实证研究

通过对32所高校调研分析发现,大学生“慢就业”现象呈现上升态势。研究表明:个人最高学历、职业规划清晰程度、就业主动性、父母对于子女选择“慢就业”的态度、是否为独生子女、家庭人均年收入、学校层次、学校就业指导服务质量等因素均显著影响大学生对于“慢就业”行为的认知与选择。为此,必须结合大学生“慢就业”行为选择的影响因素,开展“广谱式”的职业生涯规划教育、构建家校联合的就业指导机制、形成具有强针对性的就业指导体系。

决策树和Logistic回归模型对体外受精-胚胎移植患者妊娠结局的预测价值比较

目的:比较决策树和Logistic回归模型对体外受精-胚胎移植(in vitro fertilization and embryo transfer,IVF-ET)患者妊娠结局的预测价值。方法:纳入2021年1月至2022年10月在长治医学院附属和平医院接受IVF-ET的患者350例为研究对象,根据妊娠结局分为妊娠成功组(215例)和妊娠失败组(135例)。收集患者临床资料,建立IVF-ET患者妊娠结局Logistic回归和决策树预测模型,并在是否基于Logistic回归结果条件下建立决策树分析模型(决策树1和决策树2),采用受试者工作特征(receiver operating characteristic,ROC)曲线对模型预测效果进行评价。结果:350例患者中,妊娠成功患者占61.43%,妊娠失败者占38.57%。妊娠失败组年龄≥35岁、不孕年限≥5年、周期次数≥1次、有心理精神障碍的患者比例及HCG日血清孕酮水平均高于妊娠成功组,获卵数≥10枚、受精率≥75%的患者比例及HCG日子宫内膜厚度、优质胚胎数小于妊娠成功组(P<0.05)。多因素Logistic回归分析结果显示,年龄、HCG日血清孕酮水平、优质胚胎数及心理精神障碍均是IVF-ET患者妊娠结局的影响因素(P<0.05)。决策树模型显示,年龄、HCG日血清孕酮水平、优质胚胎数为IVF-ET患者妊娠结局的影响因素。Logistic回归模型曲线下面积(area under curve,AUC)为0.832,预测敏感度、特异度和准确度分别为87.3%、71.4%、83.5%;决策树1的AUC为0.859,预测敏感度、特异度和准确度分别为85.1%、76.8%、85.6%;决策树2的AUC为0.820,预测敏感度、特异度和准确度分别为83.7%、73.2%、82.4%。决策树1的AUC大于决策树2(P<0.05),但与Logistic回归模型的AUC比较差异无统计学意义(P>0.05)。结论:Logistic回归模型和决策树模型对于IVF-ET患者妊娠结局均有一定的预测价值。

105例不孕症患者子宫输卵管造影结果影响因素的Logistic回归分析

目的探讨不孕症患者子宫输卵管造影结果发生异常的影响因素。方法选取2021年1月至2023年5月在湖南中医药大学第二附属医院妇科门诊就诊并接受X线子宫输卵管造影(X-ray hysterosalpingography,X-HSG)检查的105例不孕症患者,收集患者临床资料包括年龄、月经周期、不孕症类型、支原体感染史、衣原体感染史、淋病奈瑟球菌感染史、盆腔炎相关病史、输卵管相关病史等,并填写《中医体质调查问卷表》,采用Logistic回归方程分析不孕症患者X-HSG结果的影响因素。结果X-HSG结果异常与盆腔炎相关病史、输卵管相关病史、年龄、气郁质及湿热质呈正相关(P<0.05),与不孕症类型、月经周期规律与否无相关性(P>0.05)。多因素Logistic回归分析结果显示:不孕症类型、月经周期、气郁质、湿热质是HSG发生异常的危险因素(OR>1),年龄、盆腔炎相关病史、输卵管相关病史是HSG发生异常的保护因素(OR<1)。结论适龄生育、减少盆腔炎相关病史、减少输卵管相关病史、调畅情志、忌食肥甘厚味对于减少输卵管病理损伤引起的不孕症至关重要。

影响结核性胸腔积液并发胸膜增厚的非条件Logistic回归分析

目的探讨影响结核性胸腔积液并发胸膜增厚的相关危险因素。方法回顾性分析2019年1月至2021年3月广州市胸科医院呼吸内科收治的123例结核性胸腔积液患者的临床资料,统计患者胸膜增厚情况,采用单因素及非条件Logistic回归法分析患者胸膜增厚的影响因素。结果胸膜未增厚与胸膜增厚患者性别、年龄、肺结核、胸水分布、用力肺活量(FVC)、第1秒用力呼气容积(FVE1)/FVC、胸水腺苷脱氨酶(ADA)、血清ADA、胸水白细胞、胸水淋巴细胞、胸水中性粒细胞和淋巴细胞比例比较差异无统计学意义;胸膜未增厚与胸膜增厚患者胸水量、胸水吸收时间、FVE1、胸水乳酸脱氢酶(LDH)、血清LDH、胸水蛋白、血清蛋白比较差异有统计学意义(P<0.05)。Logistic回归分析结果显示,胸水量(中大量)、FVE1、胸水LDH、血清LDH、胸水蛋白及血清蛋白是胸膜增厚发生的危险因素(P<0.05)。结论结核性胸腔积液并发胸膜增厚是多因素作用的结果,胸水量、FVE1、胸水吸收时间、胸水LDH、血清LDH、胸水蛋白和血清蛋白与胸膜增厚的发生密切相关,建议临床予以密切监测并积极采取针对性干预措施。

卵圆孔未闭合并偏头痛患者头痛程度影响因素的logistic回归分析

目的分析卵圆孔未闭(PFO)合并偏头痛患者头痛程度影响因素的logistic回归分析。方法选取2022年1—12月商丘市第一人民医院就诊的100例PFO合并偏头痛患者作为研究对象,采用视觉模拟评分法(VAS)分为轻度头痛组(58例)和中重度疼痛组(42例),采用多因素logistic回归分析影响PFO合并偏头痛患者头痛程度的因素。结果单因素分析显示,轻度头痛组和中重度疼痛组吸烟史、高血压史、睡眠质量、情绪变化、PFO右向左分流量、PFO直径、PFO隧道长度及有无房间隔膨出瘤差异有统计学意义(P<0.05)。logistic多因素回归分析显示,吸烟史、高血压史、睡眠质量差、情绪变化、PFO右向左分流量大、PFO直径大、PFO隧道短及房间隔膨出瘤是影响PFO合并偏头痛患者头痛程度的影响因素(P<0.05)。结论吸烟史、高血压史、睡眠质量差、情绪变化、PFO右向左分流量大、PFO直径大、PFO隧道短及房间隔膨出瘤可影响PFO合并偏头痛患者头痛程度,临床应根据上述因素进行针对性干预,以缓解偏头痛患者头痛程度。

双模态Logistic Regression及其应用

传统的Logistic Regression能够解决单一模态数据的二分类问题,但在处理多源异构数据时不能很好地利用不同模态间的语义相关性,从而降低了分类性能。为了对双模态数据进行建模,提出同时包含模态内语义信息和模态间语义相关性的双模态Logistic Regression模型。设计一个包含模态内损耗与模态间损耗的目标函数,利用梯度下降法优化目标函数,在每次迭代过程中该模型能够根据一定策略交替地更新不同模态的参数。实验结果表明,双模态Logistic Regression能够获得较好的分类性能和跨模态检索效果。

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.

社会融合视角下农民工城市定居意愿探赜——基于Logistic模型的实证研究

农民工的城市定居意愿影响农民工市民化进程,进而影响我国新型城镇化建设与乡村振兴。文章基于2023年873份农民工问卷,在社会融合视角下,构建二元logistic回归模型对农民工的城市定居意愿影响因素进行研究。实证研究表明,只有58%的农民工具有城市定居意愿。农民工性别、年龄、婚姻状况、教育水平、家庭随迁人口数量、城市迁移时间长短等因素对定居意愿均没有显著影响;参加本地医保、身份认同、经济收入、健康状况、承包地等因素对农民工的城市定居意愿影响较大;社会参与度、闲暇生活、居住证等在一定程度上提升农民工的定居意愿。如何持续提升农民工的城市定居意愿,文章从社会关系融合、经济收入及公共服务均等化等三个方面提出了对策建议。

Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia

Landslide susceptibility maps(LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression(LR) and an artificial neural network(ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR(FSLR), ANN, and their combination(FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher(92.59%) than LR(82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve(AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR-ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.