A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Investigation of Nuclear Binding Energy and Charge Radius Based on Random Forest Algorithm

The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model.

Companies’ E-waste Estimation Based on General Equilibrium The­ory Context and Random Forest Regression Algorithm in Cameroon: Case Study of SMEs Implementing ISO 14001:2015

Given the challenge of estimating or calculating quantities of waste electrical and electronic equipment(WEEE)in developing countries,this article focuses on predicting the WEEE generated by Cameroonian small and medium enterprises(SMEs)that are engaged in ISO 14001:2015 initiatives and consume electrical and electronic equipment(EEE)to enhance their performance and profitability.The methodology employed an exploratory approach involving the application of general equilibrium theory(GET)to contextualize the study and generate relevant parameters for deploying the random forest regression learning algorithm for predictions.Machine learning was applied to 80%of the samples for training,while simulation was conducted on the remaining 20%of samples based on quantities of EEE utilized over a specific period,utilization rates,repair rates,and average lifespans.The results demonstrate that the model’s predicted values are significantly close to the actual quantities of generated WEEE,and the model’s performance was evaluated using the mean squared error(MSE)and yielding satisfactory results.Based on this model,both companies and stakeholders can set realistic objectives for managing companies’WEEE,fostering sustainable socio-environmental practices.

基于RF-SFLA-SVM的装配式建筑高空作业工人不安全行为预警

为有效预警装配式建筑高空作业工人不安全行为的发生趋势或状态,增强对装配式建筑工人不安全行为(PBWUBs)的管控,采用随机森林(RF)-混合蛙跳算法(SFLA)-支持向量机(SVM)模型,开展工人不安全行为预警研究。首先,采用SHEL模型分析处于高空作业危险中的PBWUBs的影响因素,并通过RF确定关键预警指标;然后,采用SFLA对SVM的参数进行寻优改进;最后,利用RF-SFLA-SVM预警高空作业PBWUBs,提出应对措施,并与其他预警模型对比。研究结果表明:基于RF-SFLA-SVM预警高空作业PBWUBs,准确率最高,为91.67%,与其他模型的预警性能相比,最高提升14%。研究结果可为高空作业PBWUBs的防控提供参考。

基于RF-GWO的水利工程地质渗透系数智能反演分析

地质渗透系数是准确分析水利工程渗流的关键参数。针对传统反演方法计算效率低、精度差的问题,采用有限元正演模型和正交试验设计构建渗透系数反演样本集,建立了基于随机森林(RF)算法的渗流计算代理模型;在此基础上,引入灰狼优化(GWO)算法,提出了基于RF-GWO的渗透系数智能反演方法,并以Z抽水蓄能电站为研究案例进行了验证。结果表明:RF模型对各钻孔水位预测结果均接近实测值,性能优于CART和BP模型;GWO可搜寻到地质最佳渗透系数,钻孔水位反演结果合理,相对误差最大为0.42%,精度满足工程要求,计算的天然渗流场分布形态也符合一般山体渗流场分布规律。建立的反演模型能够快速准确地推断工程区地层渗透系数,具有实际工程应用价值。

基于K_means++聚类与RF_GRU组合模型的电力负荷预测方法研究

短期负荷预测是电力系统对负荷进行规划的重要依据之一,为了进一步提升短期负荷预测的精度,提出一种基于K_means++聚类与RF_GRU组合模型的电力负荷预测方法。首先,采用K_means++聚类算法将负荷群体分成负荷特性相近的群体,然后利用改进后的CSO算法优化随机森林中的相关参数使其性能最优,接着根据聚类情况采用随机森林选择结构不同的多层GRU网络分别对各组负荷群体进行预测,最后将所有组的预测结果相加得出最终预测值。算例结果表明,聚类算法的归纳整理功能为预测方法节省了预测时间,而采用组合模型又进一步提高了预测精度。

基于OVMD-RF方法的风力发电机滚动轴承故障诊断

风电机组运行时轴承受到交变应力和冲击载荷,振动信号非线性、不平稳且具有噪声,特征提取不充分.针对风力发电机轴承故障信号处理和特征提取的固有缺陷,提出了基于优化变分模态分解与随机森林算法结合的故障诊断方法.首先,利用乌燕鸥优化算法对变分模态分解的参数进行搜索寻优;然后,利用优化参数的变分模态分解对滚动轴承振动信号进行分解,获得模态分量;最后,以峰值、峭度和包络熵构建融合特征训练集,并输入至随机森林分类器进行模型训练,实现故障识别.实例分析的结果表明,该方法识别风力发电机轴承故障的准确率高达100%,可实现故障的准确判别.

结合遗传算法的RF-DBN入侵检测方法

针对目前不平衡数据集少数类攻击样本识别率较低的问题,提出一种BorderlineSMOTE、随机森林和遗传算法(genetic algorithm,GA)-深度信念网络(deep belief network,DBN)相结合的入侵检测方法。首先采用BorderlineSMOTE对少数类样本进行过采样,减少数据集的不平衡度;然后使用随机森林算法实现正异常数据分类,筛选出异常数据;最后采用经GA优化的DBN网络对异常数据进行进一步分类。使用网络安全数据集CICIDS2017进行验证,该方法的准确率达到了99.85%,而且少数类样本的识别精度也有明显提高。

基于自适应GA-RF的用户流失预测研究

针对电信用户流失问题,文章提出一种自适应遗传算法优化随机森林的预测模型。首先对Kaggle平台提供的电信数据进行数据清洗、特征提取及无量纲化处理,然后运用SMOTE过采样以解决数据不平衡问题,对决策树、随机森林等模型预测的召回率、F1和AUC值进行对比。最后提出一种自适应遗传算法优化随机森林的电信用户流失预测模型。结果表明,自适应遗传算法优化的随机森林模型的预测性能优于单一分类模型。

CHAID-RF:基于CHAID决策树的集成学习方法

针对卡方自动交互诊断(CHAID)决策树易过拟合的问题,提出CHAID随机森林方法(CHAID Random Forest,CHAID-RF)。该方法采用随机采样、随机选择特征以及集成的策略,将CHAID决策树作为基分类器,形成CHAID-RF。为了验证CHAID-RF的有效性,选取CART、CHAID、SVM、RF作为对比算法,以准确率、加权查准率、加权查全率、加权F值作为分类模型评价指标,以均方根误差作为回归模型评价指标,采用10个分类数据集和7个回归数据集进行验证。实验结果表明CHAID-RF可行有效。

基于RF-RNN模型的DNS隐蔽信道检测方法

为提高检测隐蔽信道的灵敏度,提出一种基于随机森林(Random Forest,RF)和循环神经网络(Recurrent Neural Network,RNN)的域名系统(Domain Name System,DNS)隐蔽信道检测方法。该方法采用域名检测作为主要手段,使用RF模型对域名进行分类,通过深度学习方法挖掘更高阶的特征表示。实验结果表明,与单一模型相比,该方法在检测准确性和健壮性方面均取得了显著提升。

基于GJO特征量优选的AO-RF的变压器故障诊断模型

在变压器故障诊断过程中,进行合理的特征优选,将有助于提高诊断模型的诊断精度,为此,文中提出了一种基于金豺优化算法(golden Jackal optimization,GJO)特征量优选与AO-RF的变压器故障诊断模型。首先,采用GJO对构建的21维变压器油中溶解气体特征量进行优选;然后,根据GJO得到的特征优选结果,采用天鹰算法(aquila optimizer,AO)优化随机森林(random forest,RF)的变压器故障诊断模型对变压器故障进行诊断,并与不同特征量、不同故障诊断模型的诊断结果进行了对比。实验结果表明:GJO优选特征量相比21维原始特征、三比值法、无编码比值法以及AO优选特征量的故障诊断准确率可提高1.12%~25.78%,kappa系数可提高0.02~0.24;AO-RF故障诊断模型较RF、SVM、ELM、SSA-RF、WOA-RF、GJO-RF模型的诊断准确率可提高1.84%~15.86%,kappa系数可提高0.02~0.16,验证了所提方法的有效性和准确性。

基于WOA-RF的边坡稳定性预测模型

为有效地预测边坡稳定性和预防边坡失稳事故的发生,提出了鲸鱼优化算法(whale optimization algorithm,WOA)和随机森林(random forest,RF)相结合的混合模型WOA-RF;基于所收集的边坡案例,采用混淆矩阵的分类性能指标和受试者工作特征曲线及线下面积评估混合模型WOA-RF的分类和泛化性能;使用WOA对4种广泛应用的机器学习模型进行优化,并将优化后的机器学习模型与WOA-RF模型进行对比分析。结果表明:WOA可以有效地优化超参数和提升模型性能;最优WOA-RF模型在训练集和测试集上的准确率分别为0.99和0.94,优化后,准确率、精确率、召回率、精确率和召回率的加权平均值分别提升了11.9%、19.0%、4.8%和11.9%;对比分析各个模型的预测性能后发现,WOA-RF模型的各项指标均优于其他模型;确定了特征重要性排序,发现容重是影响边坡稳定性的最敏感特征。WOA-RF模型可有效地预测边坡稳定性,预测结果可为防护措施的制定提供依据。

基于RF-RFE算法的地铁车站洪涝灾害预测研究

地铁车站形式以地下段为主,进入雨期时受到洪涝灾害的威胁,易发生雨水倒灌现象,严重影响居民出行和地铁安全运营。为进一步提高地铁车站洪涝灾害预测的效果,提出基于RF-RFE和DNN神经网络的地铁车站洪涝灾害预测方法。首先,通过收集并分析已发生地铁车站洪涝灾害的案例,采用文献综述结合专家访谈的方法,构建地铁车站洪涝灾害初始变量集;然后,利用随机森林—递归特征消除(RF-RFE)算法,计算初始变量重要性并完成变量分类正确率排序,从初始变量集中筛选出重要变量;最后,建立DNN神经网络预测模型,并以筛选出的重要变量作为输入样本,训练DNN神经网络,完成对地铁车站洪涝灾害的预测。研究结果表明:(1)变量选择可提高预测模型精度,与初始变量集的DNN神经网络预测模型相比,数据筛选后的DNN神经网络预测模型准确率提高了4.36%;(2)RF-RFE和DNN神经网络算法结合具有良好的效果,预测模型准确率为88.1%,F1分数为0.9。

DPCA-POA-RF-Informer在多情景光伏多步预测中的应用

针对光伏发电波动性与不确定性对电力系统稳定产生的影响,对多情景光伏发电功率的多步预测进行研究。首先通过密度峰值算法根据太阳辐射量、温度、湿度等气象数据对天气状况进行精确分类。其次,为了使模型表现出更好的性能,建立了鹈鹕算法优化随机森林(POA-RF)的因素筛选特征变量,模型用鹈鹕算法对随机森林的决策树数目和深度两个参数进行寻优,加强了因素筛选的有效性。最后,基于Informer模型对不同天气状况的光伏功率进行多步预测。实例计算结果验证了所提模型预测精准度的有效性与精准性。

A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm

As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.

基于PCA-GA-RF的矿井突水水源快速识别模型

矿井突水已成为影响矿山安全生产的主要危害之一,快速准确识别突水水源类型是矿井突水灾害治理的关键步骤。提出了1种基于PCA-GA-RF的矿井突水水源识别模型;基于安徽省颍上县谢桥煤矿的88组水样实测数据,遵循分层随机抽样的原则,按照7∶3的比例将其分为62组训练样本和26组预测样本,经PCA提取4个主成分,构建PCA-GA-RF模型,并与PCA-RF、PCA-ABC-RF和PCA-FA-RF模型对比。结果表明:PCA-GA-RF模型判别结果准确率为96.153 8%,与其他模型相比准确率、精确率、召回率和F1值(精确召回率)最高,具有优越性。

基于RF与D-S证据理论融合的多通道齿轮箱复合故障诊断

针对齿轮箱复合故障特征难于提取,诊断缺乏自动识别性,且单通道往往无法全面表征故障信息等问题,提出一种基于随机森林与证据理论相融合的多通道齿轮箱复合故障诊断方法。首先通过小波包变换对各通道复合故障信号进行分解,得到故障信号的特征向量;之后引入一种新的特征集组合框架构建针对不同故障的特征数据集,通过随机森林算法划分单个分类模型;接着综合考虑各分类模型,合成每个通道下的集成分类器,并提出一种新的迭代自更新策略不断完善分类器的性能;最后设计一种基于Lance距离的改进D-S证据理论算法,该算法采用Lance距离来度量各空间证据间的证据距离,并构造Lance矩阵,由此获得相似度矩阵来衡量各证据体间的相似程度和支持度,通过计算各通道的敏感度权重系数进行BPA修正,获得最终的诊断融合结果。通过齿轮箱试验平台进行算法验证,结果表明该方法能有效识别出复合故障中包含的每类故障,并能全面融合不同通道的故障冗余信息,实现齿轮箱复合故障的精确诊断。

Prediction of Permeability Using Random Forest and Genetic Algorithm Model

Precise recovery of CoalbedMethane(CBM)based on transparent reconstruction of geological conditions is a branch of intelligent mining.The process of permeability reconstruction,ranging from data perception to real-time data visualization,is applicable to disaster risk warning and intelligent decision-making on gas drainage.In this study,a machine learning method integrating the Random Forest(RF)and the Genetic Algorithm(GA)was established for permeability prediction in the Xishan Coalfield based on Uniaxial Compressive Strength(UCS),effective stress,temperature and gas pressure.A total of 50 sets of data collected by a self-developed apparatus were used to generate datasets for training and validating models.Statistical measures including the coefficient of determination(R2)and Root Mean Square Error(RMSE)were selected to validate and compare the predictive performances of the single RF model and the hybrid RF–GA model.Furthermore,sensitivity studies were conducted to evaluate the importance of input parameters.The results show that,the proposed RF–GA model is robust in predicting the permeability;UCS is directly correlated to permeability,while all other inputs are inversely related to permeability;the effective stress exerts the greatest impact on permeability based on importance score,followed by the temperature(or gas pressure)and UCS.The partial dependence plots,indicative of marginal utility of each feature in permeability prediction,are in line with experimental results.Thus,the proposed hybrid model(RF–GA)is capable of predicting permeability and thus beneficial to precise CBMrecovery.