Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.

Predictive Modeling for Analysis of Coronavirus Symptoms Using Logistic Regression

This paper presents a case study on the IPUMS NHIS database,which provides data from censuses and surveys on the health of the U.S.population,including data related to COVID-19.By addressing gaps in previous studies,we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms.Our experiments focus on four groups of factors:demographic,socio-economic,health condition,and related to COVID-19 vaccination.By analysing the sensitivity of the variables used to train the models and the VEC(variable effect characteristics)analysis on the variable values,we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.

A predictive model of the relationship between hematology,urine,clinical examination and the occurrence of depression risk are established based on machine learning

Background:Depression is a kind of emotional disorders caused by a variety of factors,with the accelerating pace of life,people in life and work facing competition pressure is increasing,the incidence of depression is increasing year by year,so the in-depth study of the pathogenesis of depression,and the development of depression risk prediction model is becoming increasingly important.Method:This study data is derived from the 2017–2018 follow-up data from the National Health and Nutrition Examination Survey database,a publicly available database using a multi-stage,hierarchical,clustered,probability sampling design to determine a nationally representative sample of non-institutionalized US civilians.Participants completed home interviews,laboratory measurements,and a physical examination.Details of the survey design have been published previously.This study evaluated the risk factors for the occurrence of depression from this study from multiple variables such as age,sex,and combined complications.Four machine learning algorithms(logistic regression,Lasso regression,support vector machine,random forest)were used to establish predictive classification models and compare the area under the subject operating feature curve and accuracy.The dataset was validated using a 10-fold cross-validation.Result:We excluded the invalid samples for 815 included samples,of which 570 cases were divided into the validation set and 245 cases were divided into the training set.The area under the curve(AUC)of Nomogram establishing risk of depression based on logistic regression was 0.73.Among the three machine learning models,the Lasso regression-based model AUC was 0.548,a mean AUC for support vector machines was 0.695,and a random forest AUC of 0.613.The support vector machines-based model predicted the best performance compared to other machine models.Conclusion:Random forest-based prediction models are able to assist clinicians in providing decision support when it is difficult to give an exact diagnosis.The model has good clinical utility and facilitates clinicians to identify high-risk patients and perform individualized treatment.The established four models of logistic regression,Lasso regression,support vector machine,and random forest all have good predictive power.

类别型缺失数据的聚类半参数logistic学习插补法

缺失数据插补是数据科学中的基本方法之一.本文针对类别型缺失数据提出了一种基于聚类和半参数logisitic学习模型的插补法.该方法首先采用K-近邻法对缺失数据进行预插补,然后用聚类算法将数据分类,提高数据间的相似性,再为每一类数据集建立半参数logistic学习模型,进而完成最后的插补.基于中国家庭金融调查(CHFS)数据集的实证研究表明,该方法优于常用的K-近邻插补法和随机森林插补法.

基于Logistic回归麻雀算法的图像分割

针对麻雀搜索算法后期种群多样性减少、易陷入局部最优解等问题,提出一种新的改进麻雀搜索算法。所提算法先引入小孔成像反向学习策略对发现者的位置进行更新,提升寻优位置的多样性;其次受Logistic模型的启发,提出一种新的自适应因子对安全阈值进行动态控制,平衡所提算法的全局搜索与局部开发的能力。通过与其他算法在6个基准函数上进行仿真对比,结果表明:所提算法的收敛精度与速度均优于其他算法。在工程应用上,用所提算法优化K-means聚类算法进行图像分割,峰值信噪比(PSNR)、结构相似性(SSIM)及特征相似性(FSIM)3种度量指标验证了其良好的分割性能。

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

基于多元Logistic的混合式学习效果影响因素分析

该文以西安航空学院理学院数据挖掘导论课程为例,采用问卷调查及统计方法,对影响学生混合式学习效果的16个最主要因素进行实证分析,得到学习环境、教学、个人和平台网络因子4个主成分,根据这4个主成分建立混合式学习效果的多元有序Logistic回归模型,研究表明环境因子、教学因子和个人因子这3个主成分对课程的混合式学习效果产生显著正向影响关系,并依此给出相关建议。

基于Logistic和学习曲线模型的中国电源结构预测

可再生能源发电是缓解电力短缺、化石能源过度消耗和环境污染多重压力的重要突破口。Logistic模型和学习曲线模型能很好地刻画新能源发电技术创新过程中累积产量不断接近最大生产极限的过程以及发电成本降低的过程。预测结果显示,未来40年风电和太阳能发电发展速度较快,其中风电的装机容量从2040年开始超过水电,但以火电为主的电源结构将持续。

学习障碍儿童家庭影响因素的Logistic回归分析

目的分析学习障碍儿童的家庭影响因素,为儿童学习障碍的防治提供参考依据。方法采用自拟家庭情况调查问卷对2364名学生家长进行调查,并进行单因素及多因素非条件Logistic回归分析。结果单因素Logistic回归分析显示,亲子交流(OR=0.803)、孩子在家中的学习环境(OR=0.626)、家庭经济收入(OR=0.775)、父亲文化程度(OR=0.590)、母亲文化程度(OR=0.582)是儿童学习障碍的保护因素;娇宠型教育方式(OR=3.710)是儿童学习障碍的危险因素。多因素Logistic回归显示,父亲文化程度(OR=0.721)、母亲文化程度(OR=0.753)和娇宠型教育方式(OR=4.007)是家庭因素中儿童学习障碍的主要影响因素。结论家庭因素对儿童学习障碍的影响不可忽视,提高父母文化素质、改善教育方式对防治儿童学习障碍具有重要的意义。

基于Logistic模型的改进人工蜂群算法

针对基本人工蜂群(ABC)算法存在着收敛速度慢、易陷入局部最优、求解精度低等缺点,提出一种基于Logistic模型的自适应人工蜂群(A-ABC)算法.首先,利用反向学习策略初始化种群个体以保证群体的多样性,为算法进行全局搜索奠定基础;其次,通过引入参数λ对ABC算法的搜索方程进行改进以产生新的候选个体,在进化过程中,λ的大小基于Logistic模型自适应调节,以协调算法的探索能力和开发能力;引入基于排序的选择概率以避免算法出现早熟收敛.对几个典型的Benchmark函数进行了测试,实验结果表明,与基本ABC算法相比,A-ABC算法具有更高的求解精度和更快的收敛速度.

基于Logistic回归模型和凝聚函数的多示例学习算法

鉴于很多实际问题都可以转化到多示例框架下求解,多示例学习越来越受到机器学习领域内学者们的关注.提出了一个基于Logistic回归模型的多示例学习算法.首先定义了一个新的似然函数来表示每个包的标签与其示例的隐含标签之间的关系,然后利用凝聚函数把该似然函数转化为一个光滑的凹函数,从而使问题可以用常用的无约束优化方法快速求解.在一些标准数据集和一个文本分类问题上的实验结果表明,所提算法要优于其他常用多示例学习算法.

基于二元logistic模型的大学生网络学习行为影响因素研究

基于河北大学704名经济类本科生的问卷调查数据,应用二元logistic回归模型对大学生网络学习行为的影响因素进行了研究。研究结果表明优化资源共享、增强学习约束力、增加网络学习时间、明确学习目标、提高学习积极性、加强学习互动,均有利于促进大学生网络学习期间专心学习行为的发生。疲劳度对网络学习行为的选择不存在显著影响,但学习时间与疲劳度的交互作用容易导致分心学习或不学习行为的发生,表现为二者的交互作用越强,专心学习行为发生的机会比率越低。

儿童学习困难危险因素的条件logistic回归分析

目的 探讨儿童学习困难危险因素。方法 对 10 8名学习困难儿童与 10 8名学习优良儿童进行 1∶1配对对照研究 ,运用条件Logistic回归模型。结果 19项指标回归分析中有 5项指标有显著性意义 (P <0 .0 5 )。结果表明 ,儿童学习困难发生的危险因素依次为 :学习兴趣、言语智商、心理卫生问题、注意力和父母关系。结论 克服儿童学习困难发生的各种危险因素 ,可提高儿童学习成绩。

基于非线性Logistic模型的改进UDEED算法

针对UDEED算法中线性Logistic模型分类预测准确率较低的问题,基于泰勒展开式,提出一种多项式核的非线性Logistic模型改进算法。研究非线性Logistic模型的核函数参数估计方法,更新损失函数的计算规则,并利用梯度下降法求解改进UDEED模型,实现数据集的分类预测。实验结果表明,与UDEED算法相比,改进算法提高了分类预测的准确率。

有序Lasso-Logistic模型的电竞角色选择应用分析

电竞行业近几年来迅速发展,这其中离不开机器学习在电竞中的分析和应用。职业选手能够熟悉电竞角色往往需要一定的时间和经验,采用机器学习对电竞角色进行分析,有利于选手对角色选择的考虑,为职业选手的训练和比赛提供数据支持,体现预测模型的分析效果。基于电竞结果通常为二分类数据,为了更好地利用先验信息,将自变量对因变量发生的不同重要性归结到Lasso-Logistic模型中形成有序Lasso-Logistic模型,通过有序Lasso-Logistic模型分析电竞角色数据,对电竞结果进行预测。根据结果显示,有序Lasso-Logistic模型预测效果显著,将有序Lasso-Logistic模型预测结果与逻辑回归模型,Lasso-Logistic模型,梯度增强决策树模型以及SVM模型的预测结果相比较,增加了先验信息的有序Lasso-Logistic模型的预测效果明显比其他四个机器学习模型更佳,表明了有序Lasso-Logistic模型在机器学习中对分类数据相比经典分类模型具有更好的分析能力,突出自变量先验信息重要的作用,推动了机器学习在电子竞技行业的应用与发展。

数据增广求解贝叶斯Logistic回归模型的方法研究

贝叶斯Logistic回归模型是机器学习中一类被广泛应用的经典模型,然而由于其先验和似然间的非共轭性,Logistic回归模型的贝叶斯推理成为机器学习中的一个重要问题。数据增广方法是一种解决非共轭问题非常有效的方法,该方法通过引入增广变量来发掘模型中的隐藏结构,再通过采样的方法得到模型推理结果。本篇文章实现了两种不同的数据增广算法并通过在多个现实生活数据集上进行试验来对比算法的优越性。

基于迁移学习和逻辑回归模型的花卉分类研究

【目的】不同种花卉之间的相似性以及同种花卉内部的多变性加大了花卉图像分类难度,其难点是要人工设计出能充分体现花卉颜色、形状和花瓣形态等特征的特征提取方法。传统的花卉图像分类方法的精度不高且模型的泛化能力较差,这些问题亟待解决。【方法】课题组提出一种基于数据增强的VGG16迁移学习卷积神经网络提取花卉图像特征,再训练多类逻辑回归模型的花卉图像分类识别方法;并且通过在flowers17和flowers102花卉数据集上进行测试,来验证课题组所提出的花卉分类识别方法的有效性。【结果】课题组所提出的花卉分类识别方法在flowers17和flowers102数据集中分别达到了97.89%和92.10%的分类精度,高于现有其他花卉图像分类方法。【结论】通过预训练的深度人工神经网络提取的高区分度的花卉图像特征,优于人工设定的花卉图像特征,能训练出更高效精准的花卉识别分类器。基于本研究内容,下一步可对VGG16网络进行降维改进,让模型参数减少,从而实现快速实时应用。

基于联合神经网络的投诉预测模型研究

对影响电信运营商重复投诉的关键因素进行深入探讨,旨在提高服务质量并构建风险预测模型。基于运营商客服数据,研究采用了Logistic回归、BP神经网络以及二者联合建模的方法。Logistic回归模型确定了5个主要影响因素,预测重复投诉发生的概率,精度达到80.0%。BP神经网络则选取了81个影响因素,预测精度为90.6%。在此基础上,构建了联合模型,其精度高达92.8%。实际应用于某省会电信运营商后,重复投诉率下降了3.2%,成效显著,为提高电信运营商服务质量、降低重复投诉率提供了有力支持,对我国电信行业发展具有重要意义。