Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

Travel time prediction model of freeway based on gradient boosting decision tree

To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.

Tactical intention recognition of aerial target based on XGBoost decision tree

In order to improve the accuracy of target intent recognition,a recognition method based on XGBoost(eXtreme Gradient Boosting)decision tree is proposed.This paper adopts relevant data and program of python to calculate the probability of tactical intention.Then the sequence intention probability is obtained by applying Dempster-Shafer rule of combination.To verify the accuracy of recognition results,we compare the experimental results of this paper with the results in the literatures.The experiment shows that the probability of tactical intention recognition through this method is improved,so this method is feasible.

Stability prediction of underground entry-type excavations based on particle swarm optimization and gradient boosting decision tree

The stability of underground entry-type excavations will directly affect the working environment and the safety of staff.Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations.Therefore,this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph.Accordingly,the particle swarm optimization(PSO)algorithm is used to optimize the core parameters of the gradient boosting decision tree(GBDT),abbreviated as PSO-GBDT.Moreover,the classification performance of eight other classifiers including GDBT,k-nearest neighbors(KNN),two kinds of support vector machines(SVM),Gaussian naive Bayes(GNB),logistic regression(LR)and linear discriminant analysis(LDA)are also applied to compare with the proposed model.Findings revealed that compared with the other eight models,the prediction performance of PSO-GBDT is undoubtedly the most reliable,and its classification accuracy is up to 0.93.Therefore,this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations.In addition,each classification model is used to predict the stability category of several grid points divided by the critical span graph,and the updated critical span graph of each model is discussed in combination with previous studies.The results show that the PSO-GBDT model has the advantages of being scientific,accurate and efficient in updating the critical span graph,and its output decision boundary has strict theoretical support,which can help mine operators make favorable economic decisions.

Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card,loan defaulters and similar types is achievable with the assistance of Machine Learning(ML)algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions.Fraudulent cases are scant in the comparison of non-fraudulent observations,almost in all the datasets.In such cases detecting fraudulent transaction are quite difficult.The most effective way to prevent loan default is to identify non-performing loans as soon as possible.Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.In this paper,the rendering of different machine learning algorithms such as Decision Tree,Random Forest,linear regression,and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations.Further model accuracy metric have been performed with confusion matrix and calculation of accuracy,precision,recall and F-1 score along with Receiver Operating Characteristic(ROC)curves.

改进灰狼算法优化GBDT在PM_(2.5)预测中的应用

针对灰狼算法易陷入局部最优解和全局搜索能力不足的问题,通过霍尔顿序列(Halton Sequence)搜索算法初始化狼群位置,避免灰狼算法陷入局部最优解和重复运算;引入莱维飞行和随机游动策略对灰狼算法的寻优过程进行优化,以增加算法的全局搜索能力;利用粒子群算法模拟灰狼种群得出的最佳适应度以用于惩罚项改进灰狼算法中的头狼更新策略。使用改进算法优化的梯度提升树(Gradient Boosting Decision Trees,GBDT)模型对北京市大气污染物监测数据中PM_(2.5)质量浓度进行预测,采用3种评估函数对各模型以及混合模型预测效果得分进行评估。结果显示,本文改进的灰狼算法对梯度提升树的优化效果优于其他算法,均方根误差E RMS为6.65μg/m^(3),平均绝对值误差E MA为3.20μg/m^(3),拟合优度(R^(2))为99%,比传统灰狼算法优化结果的均方根误差减少了19.19μg/m^(3),平均绝对值误差降低了10.03μg/m^(3),拟合优度增加了9百分点;与霍尔顿序列和莱维飞行改进的(Levy Flight-Halton Sequence,LHGWO)相比,改进的灰狼算法预测得分的均方根误差降低了10.39μg/m^(3),平均绝对值误差减小了6.71μg/m^(3),拟合优度提高了5百分点。研究表明了预测模型优化的有效性,为未来城市改善空气质量提供了科学依据和技术支持。

执行时间预测驱动的工作流作业调度

针对工作流作业调度问题,提出使用关键路径法进行工作流的执行时间预测和资源分配。工作流执行时间预测算法使用并行应用有向无环图描述工作流中子作业的执行顺序。基于此顺序,为子作业进行系统资源的逻辑分配。根据子作业的特征和资源分配信息,使用梯度提升决策树进行子作业执行时间预测,并计算工作流的关键路径。关键路径上所有子作业的完成时间之和即为工作流的执行时间。若预测的工作流执行时间满足用户要求,则根据子作业执行顺序和资源分配方案进行作业调度,执行工作流。对比实验表明,两个工作流的执行时间预测误差分别为5.72%和1.57%。与Spark默认调度算法相比,工作流调度算法将两个工作流的完成时间分别缩短了15.71%和15.44%。

基于GBDT算法的基桩竖向承载力预测方法

目的为研究支撑-半刚接钢框架结构体系的抗震性能,方法设计了一榀由嵌套式单边螺栓与T型钢构成的半刚性梁柱节点的中心支撑钢框架,并进行了拟静力试验与有限元数值模拟,通过观测整个试验现象,分析了其滞回、承载力、刚度退化、耗能等抗震指标。结果结果表明:试件破坏过程明显经历了弹性段、塑性段、破坏段三个阶段,试件破坏模式主要为支撑受压失稳破坏,塑性变形主要累积在支撑体系上,整体呈现延性破坏特征;支撑断裂后,梁柱及T型钢节点无明显塑性变形,钢框架仍具有较高的安全储备,符合“强节点、弱构件”设计原则,表明了结构具有两道抗震防线;结论支撑与半刚接钢框架协同工作使得试件具有较高的抗侧刚度抵抗水平变形,且承载力较高、滞回性能稳定、耗能能力优良;单边螺栓在试验过程中的受力性能较普通高强螺栓并无较大差别,未出现严重的预紧力松弛现象,并能高效的保持螺栓预紧力。通过有限元数值模拟分析可知,减小支撑长细比,虽能有效提高结构的抗震性能,但长细比较小会导致支撑刚度增大,加速其余构件的损坏。故应以考虑结构的延性为前提,降低支撑的长细比,才能有效提高结构的抗震性能。

基于BiLSTM-XGBoost混合模型的储层岩性识别

储层岩性分类是地质研究基础,基于数据驱动的机器学习模型虽然能较好地识别储层岩性,但由于测井数据是特殊的序列数据,模型很难有效提取数据的空间相关性,造成模型对储层识别仍存在不足.针对此问题,本文结合双向长短期循环神经网络(bidirectional long short-term memory,BiLSTM)和极端梯度提升决策树(extreme gradient boosting decision tree,XGBoost),提出双向记忆极端梯度提升(BiLSTM-XGBoost,BiXGB)模型预测储层岩性.该模型在传统XGBoost基础上融入了BiLSTM,大大增强了模型对测井数据的特征提取能力.BiXGB模型使用BiLSTM对测井数据进行特征提取,将提取到的特征传递给XGBoost分类模型进行训练和预测.将BiXGB模型应用于储层岩性数据集时,模型预测的总体精度达到了91%.为了进一步验证模型的准确性和稳定性,将模型应用于UCI公开的Occupancy序列数据集,结果显示模型的预测总体精度也高达93%.相较于其他机器学习模型,BiXGB模型能准确地对序列数据进行分类,提高了储层岩性的识别精度,满足了油气勘探的实际需要,为储层岩性识别提供了新的方法.

职住地建成环境对轨道交通通勤行为的非线性影响机制研究——以武汉市为例

改革开放以来中国大城市快速蔓延导致了职住分离及交通拥堵等问题,倡导轨道交通等绿色交通方式出行成为大城市缓解通勤压力的重要手段。当前研究主要以居住地作为地理背景考察建成环境对轨道交通通勤的作用,但较少探索就业地建成环境的影响。本研究以武汉市为例,通过189个轨道站点的刷卡数据识别轨道交通通勤人员并构建通勤出行链,通过梯度提升决策树(GBDT)解析职住地涉及建成环境和轨道站点的30个特征因素对通勤客流量和通勤时间的非线性影响机制。研究发现:①居住地通勤客流量表现为较集中的点状分布,就业地通勤客流量则呈现显著的“中心-外围”结构,职住地两端通勤时间的空间格局均呈中间低、外围高的分布特征;②职住地周边建成环境因素对轨道交通通勤客流量和通勤时间普遍存在非线性影响及阈值效应,整体而言居住地建成环境的相对影响程度高于就业地,但就业地“公司企业数”是影响通勤客流最重要的要素,就业地“与城市中心距离”则是影响通勤时间的首要因素;③职住地周边同一建成环境因素,对于通勤客流量和通勤时间的影响可能截然相反,这与城市轨道交通网络建设和社会实际发展状况相关。研究结果可以更好地理解职住地建成环境与轨道交通通勤行为的关联,有助于促进城市轨道交通和城市职住空间的协调发展。

高铁枢纽与城市轨道交通换乘流线仿真与优化

针对高铁枢纽与城市轨道交通换乘流线优化问题,以南京南站作为研究对象,建立全过程仿真模型,识别换乘瓶颈,采用梯度提升决策树法(Gradient Boosting Decision Tree,GBDT)确定参数重要度,提出改善方案.首先,分解换乘过程的行人与设施流线,分析高速铁路到达客流与城市轨道交通客流的分布特征.其次,使用AnyLogic软件建立高铁枢纽换乘城市轨道交通的全过程仿真模型,分析现状仿真结果,识别空间瓶颈.然后,设计不同优化类型下的措施参数及调整范围,形成不同参数组合方案.采取梯度提升决策树算法,识别不同措施参数的相对重要度,并据此确定改善措施的优先级.最后,依据措施的优先级,确定不同类型下的优化组合方案,选择机器学习全局可解释性方法对其进行优化效果分析,为不同场景下的服务改善提出建议.研究结果表明:换乘瓶颈主要集中于楼/扶梯通道设施以及闸机、售检票机等服务设施处;乘客换乘城轨的购票比例对平均换乘时间和单位时间最大换乘人数均起到重要影响,对于平均换乘时间,城轨自动售票机数量、购票时间、城轨进站服务时间、城轨进站闸机数量的影响相对较大,对于单位时间最大换乘人数,购票时间与城轨进站闸机服务时间的影响相对较大.为提高高铁枢纽换乘效率,建议推广电子客票和多种支付方式,优化购票及检票设施.

土地利用与城市轨道交通客流的非线性关系

城市轨道交通站点影响范围内土地利用对客流影响具有时空分异特征且存在类型差异,为针对性探讨不同站点两者的复杂非线性关系,提出一种基于土地利用空间分布规律、对站点实际影响范围进行差异化识别的方法;并通过分时段多尺度地理加权回归,获取能够表征土地利用对客流影响时空变化特征的站点聚类指标,采用K-means++算法将研究区域内的站点划分为4类;进而基于改进的梯度提升决策树模型分类定量探讨不同类别下土地利用与轨道交通客流的复杂非线性关系。研究表明:通过捕捉不同站点土地利用与客流的时空分异特征对站点进行分类识别,可有效提升两者非线性关系模型的解释度。根据模型输出结果,发现不同类别站点影响轨道交通客流的关键土地利用要素不同,第1类中关键变量为相对重要性分别为61.35%和30.08%的公交站点数量和慢行密度;第4类的情况类似但相对数值有所变化,公交站点数量的相对重要性由61.35%下降至30.31%;建筑密度在第2类中以66.57%的相对重要度占据最大比例;但在第3类中仅占5.59%。此外,不同类别站点影响范围内土地利用与轨道交通客流的关系存在较为显著且各异的阈值效应。研究表明,对于不同类别站点的用地开发应各有侧重,且应结合实际将土地利用设计指标控制在相应的合理范围内。研究为差异化的站点周边土地利用开发策略的制定提供了理论支持和量化指导。

基于联邦学习和多方安全计算的海铁联运数据安全共享方法研究

我国海铁联运占港口集疏运比例仍然过低,关键原因之一在于铁路对于港口适运货源的动态信息不明、营销组织不力。铁路货运营销部门缺乏在保证港口、铁路、海关三方数据隐私安全的前提下,根据铁路运力动态主动挖掘港口和海关数据中潜在适运货源的技术方法和手段,难以推出适销对路的运输产品和动态营销手段,也难以为海铁联运基础设施的建设提供有效决策依据。构建基于联邦学习和多方安全计算的铁路-港口-海关数据安全共享方法,使用结合同态加密等多方安全计算技术的梯度提升决策树作为模型训练算法,铁路、港口、海关三方地位对等协作,训练出海铁联运潜在货源识别策略;在该策略的正式运行实现过程中,铁路方能够获得路网各流向潜在适运货源的数量级,各方均看不见、带不走其余参与方的任何原始数据。

基于聚类和GBDT的镀锌钢卷力学性能预测

热镀锌钢卷力学性能影响因素之间关系复杂,限制了模型精度的提升。采用k-means算法利用化学成分属性对镀锌钢卷数据集进行聚类,将数据聚成三种模式簇实现样本的优选。利用梯度提升树算法,开展各模式数据集与不划分模式的全数据集下的力学性能建模研究,最后结合网格搜索与交叉验证方法进行模型参数优化。研究结果表明,分模式下模型MAE误差相比于全数据集建模平均减小0.85 MPa。参数优化后,各模式下MAE误差平均减少5.19 MPa,RMSE误差平均减少3.63 MPa,提高了预测模型精度。

基于LightGBM的智能可穿戴设备用户行为预测

智能可穿戴设备产生的大量数据是人类宝贵的数字资源。使用开放数据集和主流数据分析工具,如可进行快速模型开发的PyCaret模块,有助于人们进行数据挖掘工作,且不被细节所困扰。作为Kaggle竞赛爱好者的常用工具,LightGBM分类器对用户行为的预测表现优异,对此文中的研究结果也得到验证。

基于GBDT的望奎县农田土壤有机碳主控因子研究

利用多源公开数据,针对于2012年搜集到的农田土壤有机碳(SOC)数据,通过梯度增强决策树(GBDT)探究SOC的主控因子,揭示SOC空间分异机理.选择国家商品粮食生产基地县和全国产量大县-望奎县作为研究区域,结果表明,GBDT模型相较于一元线性回归模型在所有的自变量上均具有更好的预测效果,总体的R^(2)为0.958,表明该模型能够很好地解释目标变量的变异;碱解氮、有效磷、气温、土壤微生物多样性、速效钾、土壤pH和降水量依次是影响有机碳含量最为显著的几个因素,其中碱解氮以33.85%的影响权重位居最高;7个环境协变量均观察到对SOC的阈值效应,且均具有两个阈值.研究发现任意两个变量都不是独立的,均存在相互作用的负值效应.表明环境变量对SOC含量的影响是一个复杂的交互作用,而不是简单的叠加.侧面证明了过度施肥并不会达到增加土壤肥力的作用,而会造成资源浪费和农田生态污染问题.

管理层讨论与分析能预示企业违约吗?——基于中国股市的实证分析

采用文本挖掘技术,对上市公司年报中的管理层讨论与分析(MD&A)内容进行文本分析,从文本相似度、文本可读性、文本语调以及管理层预期的角度构建了MD&A评价体系。通过构建代价敏感GBDT(csGBDT)模型,考察多维管理层讨论与分析指标对企业违约预测的影响,并进一步分析了对企业违约状态有重要影响的MD&A指标及其对违约状态作用的边际效应。研究表明:MD&A指标可以作为替代性数据源准确预测上市公司违约状态;MD&A指标相比传统违约预测变量的预测效果较差;MD&A指标在传统违约判别指标基础上提供了额外的信息含量;csGBDT模型显著提高了对企业(尤其是对违约企业)的判别能力,在违约预测的大数据方法中具有明显优势。在众多管理层讨论与分析指标中,对企业违约有重要影响的MD&A指标依次为:与前一年相比文本相似度、词汇总量、情感语调2、词汇总量/句子数量、情感语调1和管理层是否发出业绩预测。本文将企业违约预测的研究边界从结构化数据拓展到非结构化文本数据,有助于抑制信息不对称导致的企业违约风险。

一种复杂环境下UWB测距误差预测方法

针对复杂室内环境中,多径干扰和视距遮挡影响室内定位精度的问题,提出一种复杂环境下UWB测距误差的预测方法:根据超宽带(UWB)室内定位方法抗干扰能力强、定位精度高的特点,从理论上分析影响超宽带信号测距精度的因素;然后通过模型量化分析每种影响因素对测距误差的影响程度,改进以往通过信道脉冲响应特征分析单一的视距遮挡参数来提高定位精度的补偿算法;最后从数据挖掘和机器学习的角度出发,将多径效应、视距遮挡、标签与基站的距离、标签移动速度、标签与基站天线俯仰角以及气象因素(温度、湿度、压强)等对测距精度造成影响的重要因素作为特征进行分类和归类,并使用梯度提升决策树模型对数据集进行训练和预测。实验结果表明,该模型可以根据当前状态下的组合特征值估计出测距误差值,将当前预测的误差值补偿到测量值上,可以有效提高超宽带室内定位的精度。