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.

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.

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.

Object-based classification of hyperspectral data using Random Forest algorithm

This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.

Improved Random Forest Algorithm Based on Adaptive Step Size Artificial Bee Colony Optimization

The traditional random forest algorithm works along with unbalanced data,cannot achieve satisfactory prediction results for minority class,and suffers from the parameter selection dilemma.In view of this problem,this paper proposes an unbalanced accuracy weighted random forest algorithm(UAW_RF)based on the adaptive step size artificial bee colony optimization.It combines the ideas of decision tree optimization,sampling selection,and weighted voting to improve the ability of stochastic forest algorithm when dealing with biased data classification.The adaptive step size and the optimal solution were introduced to improve the position updating formula of the artificial bee colony algorithm,and then the parameter combination of the random forest algorithm was iteratively optimized with the advantages of the algorithm.Experimental results show satisfactory accuracies and prove that the method can effectively improve the classification accuracy of the random forest algorithm.

The Comparison between Random Forest and Support Vector Machine Algorithm for Predicting β-Hairpin Motifs in Proteins

Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Multiscalar topography influence on soil distribution has a complex pattern that is related to overlay of pedological processes which occurred at different times, and these driving forces are correlated with many geomorphologic scales. In this sense, the present study tested the hypothesis whether multiscale geomorphometric generalized covariables can improve pedometric modeling. To achieve this goal, this case study applied the Random Forest algorithm to a multiscale geomorphometric database to predict soil surface attributes. The study area is in phanerozoic sedimentary basins, in the Alter do Chão geological formation, Eastern Amazon, Brazil. The multiscale geomorphometric generalization was applied at general and specific geomorphometric covariables, producing groups for each scale combination. The modeling was run using Random Forest for A-horizon thickness, pH, silt and sand content. For model evaluation, visual analysis of digital maps, metrics of forest structures and effect of variables on prediction were used. For evaluation of soil textural classifications, the confusion matrix with a Kappa index, and the user’s and producer’s accuracies were employed. The geomorphometry generalization tends to smooth curvatures and produces identifiable geomorphic representations at sub-watershed and watershed levels. The forest structures and effect of variables on prediction are in agreement with pedological knowledge. The multiscale geomorphometric generalized covariables improved accuracy metrics of soil surface texture classification, with the Kappa Index going from 43% to 62%. Therefore, it can be argued that topography influences soil distribution at combined coarser spatial scales and is able to predict soil particle size contents in the studied watershed. Future development of the multiscale geomorphometric generalization framework could include generalization methods concerning preservation of features, landform classification adaptable at multiple scales.

基于Isolation Forest和Random Forest相结合的智能电网时间序列数据异常检测算法

智能电网的信息系统是保障电力行业正常运行的基础,而智能电网中各种时间序列数据的分析结果是衡量信息系统稳定运行的重要依据。传统的时间序列数据异常检测算法很难同时兼顾准确性和实时性。本文引入基于Isolation Forest和Random Forest相结合的智能电网时间序列数据异常检测算法,结合无监督学习算法和有监督学习算法的优点,实现机器自动标注和自动学习阈值,人工标注少量特征值,从一定程度上提高了时间序列数据异常检查准确性和实时性,可以满足智能电网时间序列数据异常检测需求,从而达到提升智能电网信息安全的目的。

基于Sentinel-2影像的巴尔托洛冰川冰面湖研究

冰面湖是冰川的重要组成部分,是冰川消融的指示器,不仅对全球气候变化响应迅速,而且对了解和掌握区域水资源信息意义重大。本文基于Sentinel-2遥感数据,利用随机森林算法,对巴尔托洛冰川冰面湖进行识别提取,并基于提取结果分析研究区冰面湖的空间分布特征,以及冰面湖面积、数量与冰川高程的关系。本文冰面湖提取的准确率达96.07%,完整率达92.18%,错误率为11.59%;识别出巴尔托洛冰川冰面湖567个,面积为249.46~37134 m^(2);冰面湖多分布在距冰川末端3~26 km处,其中海拔3800~4300 m之间冰面湖数量最多,面积普遍较大,平均面积为1922 m^(2);随着高程的升高,冰面湖的数量和面积逐渐减少,在高程5300 m以上冰面湖数量仅为15个,平均面积为356 m^(2);高程升高导致冰面温度降低,是冰面湖数量和面积骤减的主要原因。

柴油机Wiebe模型参数优化及燃烧性能预测

基于一台单缸柴油机进行了发动机性能实验,通过结合单、双Wiebe燃烧模型和机器学习算法,提出了一种可预测的Wiebe燃烧模型,开展了不同边界条件下的燃烧参数和规律预测研究.首先,使用代数化Wiebe方程的线性拟合,根据线性拟合精度选取单、双Wiebe模型.然后,使用列文伯格-马夸尔特(Levenberg-Marquardt,LM)算法拟合Wiebe方程得到相应的6个Wiebe参数,实现放热率Wiebe参数化.最后,基于该Wiebe燃烧参数,应用误差反向传播神经网络(back propagation neural network,BP-NN)和随机森林(random forest,RF)算法,开发了实用性更广泛的两种Wiebe燃烧预测模型,研究了不同边界条件下的燃烧规律.结果显示:代数Wiebe方程的线性拟合精度小于等于0.99000时放热率曲线更复杂,此时选用双Wiebe方程可得到高精度的Wiebe燃烧参数,反之选用单Wiebe方程即可;在1200 r/min和2200 r/min时选择双Wiebe方程对放热率进行拟合,拟合精度R^(2)均大于0.99000,误差平方和均小于0.01,通过Wiebe参数重新构建的放热率和实验放热率基本一致.基于LM算法的放热率拟合算法,可以很好地反映柴油机不同工况下的燃烧特征.对比两种不同的燃烧预测模型BP-NN和RF发现:BP-NN模型对一Wiebe形状因子m1和一Wiebe燃烧初始相位φ_(01)的预测精度更高,而RF算法对一Wiebe燃烧比例α和燃烧结束相位φ_(end)的预测精度更高,因此,针对不同燃烧参数选择不同预测模型可以有效提高Wiebe燃烧预测模型的精度.

一种随机森林增强的车载容迟网络路由算法

针对车载容迟网络(Vehicular Delay Tolerant Network,VDTN)中车辆节点高速移动造成的通信链路不稳定性问题,利用车辆节点的移动模式,提出了一种随机森林增强的VDTN路由算法.首先,引入与车辆节点运动相联系的属性并利用动态相遇奖励机制对车辆节点进行分类,以此构建初始随机森林模型.接着,从决策树的分类性能和多样性两个方面优化模型,选择分类性能好、多样性高的决策树构造改进的随机森林模型,其中,决策树的分类性能和多样性分别根据每棵树分类错误率及相应的惩罚权重和由不合度量定义的决策树之间的相似度来衡量.最后,根据改进的随机森林模型提出新的VDTN路由算法.仿真实验证明,所提出的路由算法能显著提高消息的投递率,降低消息的投递时延,从而验证了其有效性.

基于人工智能方法的隧道塌方风险预测研究

为了对隧道塌方风险展开研究,整理246起隧道塌方事故案例,通过建立塌方风险评估指标体系,基于人工智能预测方法,分别采用随机森林算法、径向基函数神经网络、BP神经网络模型、粒子群算法优化BP神经网络模型,对塌方风险进行预测。结果表明,随机森林算法、径向基函数神经网络、BP神经网络模型、粒子群算法优化BP神经网络模型的塌方预测准确率分别为81.67%、83.33%、86.67%、93.33%,F_(1)值分别为0.645、0.642、0.5、0.833。粒子群算法优化BP神经网络模型预测准确率和F_(1)值均大幅提高,预测效果最好,大大减少了评估结果的主观性,为隧道塌方风险研究提供了新的研究思路。

基于不同算法筛选糖尿病足溃疡截肢预测模型的比较

目的 探讨不同算法筛选的糖尿病足溃疡(DFU)截肢预测模型。方法 收集2015年1月至2020年12月新疆医科大学第一附属医院收治的364例DFU患者的临床资料,按照截肢情况将其分为截肢组(n=213)和非截肢组(n=151),分别通过单因素分析、Boruta算法和随机森林-递归特征消除(RF-RFE)算法进行截肢危险因素分析,并构建临床预测模型,比较模型的c指数、F1分数和Brier分数,评估模型的预测效能和临床意义。结果 两组患者年龄、高血压病程、冠心病病程、Wagner评分、部位-缺血-神经病变-细菌感染-面积-深度(SINBAD)评分、国际糖尿病足工作组(IWGDF)分级比较,差异均有统计学意义(P﹤0.05)。实验室指标中截肢组患者低密度脂蛋白(LDL)、高密度脂蛋白(HDL)、甘油三酯(TG)、血钙、血磷、白蛋白与球蛋白比值(A/G)、平均血小板分布宽度(PDW)、血红蛋白(Hb)均低于非截肢组患者,截肢组患者球蛋白(GB)、中性粒细胞比例(N)、纤维蛋白原(FIB)、国际标准化比值(INR)、平均红细胞分布宽度(RDW)/白蛋白比率、中性粒细胞/淋巴细胞比值(NLR)、血小板与淋巴细胞比值(PLR)均高于非截肢组患者,差异均有统计学意义(P﹤0.05)。多因素分析结果显示,Wagner分级﹥2级、SINBAD评分﹥3分、FIB、Hb、PDW、INR、年龄均是DFU患者截肢的独立危险因素(P﹤0.05)。传统Logistic回归模型c指数、F1分数和Brier分数分别为0.771、0.809、0.163。采用Boruta算法得出对截肢影响最大的影响因素为年龄、Wagner分级﹥2级、SINBAD评分﹥3分、IWGDF分级﹥3级、A/G、INR、FIB、N、Hb、RDW比白蛋白比率、NLR和PLR,模型c指数、F1分数、Brier分数分别为0.686、0.744、0.163.RF-RFE算法得出DFU截肢危险因素为NLR、PLR、N、肌酐和PDW,模型c指数、F1分数和Brier分数分别为0.748、0.769、0.220。结论 不同算法从不同逻辑对DFU患者截肢的危险因素进行评估,可与传统统计学方法结合,为DFU的治疗决策提供依据互补。

一种基于随机森林的OFDM系统自适应算法

针对动态变化的信道环境,自适应正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统可以对子载波间隔和循环前缀长度进行调整,以最大化系统的吞吐量。为了能够快速准确地找到OFDM系统在不同信道环境中的最优子载波间隔和循环前缀长度取值,本文提出了基于随机森林的OFDM系统自适应算法。随机森林算法基于集成的思想,能够有效处理高维度数据,并且具有高效率、高准确率和强泛化能力等优势,可以在复杂的数据场景下进行有效的分类。通过提取通信过程中信噪比、用户移动速度、最大多普勒频率和均方根时延扩展等信道特征与OFDM系统的子载波间隔和循环前缀长度组成训练样本,利用随机森林算法创建了OFDM系统参数多分类模型。所提模型可以根据输入的信道特征,实现OFDM系统子载波间隔和循环前缀长度的自适应分配。同时,针对训练样本主要集中在少数几个系统参数类别的情况,利用合成少数类过采样技术对较少样本数的类别进行扩充,满足了随机森林算法对训练样本类别平衡化的需求,进一步提高了算法的分类准确率。相比传统的自适应算法,所提算法具有更高的分类准确率和模型泛化能力。分析和仿真结果表明,与子载波间隔和循环前缀长度固定的OFDM系统相比,本文所提出的自适应算法能够准确选择出最优的系统参数,可以有效地减轻信道中符号间干扰和子载波间干扰的影响,从而在整个信噪比范围上提供最大的平均频谱效率。基于随机森林的OFDM系统自适应算法能够动态地分配子载波间隔和循环前缀长度,增强OFDM系统的通信质量和抗干扰能力,实现在不同信道环境下的可靠传输。

陆浑灌区实际蒸散发影响因素分析

实际蒸散发是水文循环的关键环节,分析灌区实际蒸散发及其影响因素对灌区水资源的高效利用和农业高质量发展具有重要意义。然而,目前蒸散发的影响因素研究在确定主要因素时往往采用解释力较差的传统统计学方法,在相关性分析时忽略了蒸散发与其影响因素在空间上的相关性。因此利用改进的随机森林模型确定实际蒸散发的主要影响因素,并通过岭回归模型和地理加权回归模型探究实际蒸散发与其影响因素的时空相关关系。结果表明:(1)在灌溉期,地表净辐射、平均气温、叶面积指数和实际水汽压是实际蒸散发的主要影响因素;在非灌溉期,地表净辐射、平均气温、风速和日照时间是实际蒸散发的主要影响因素。实际蒸散发在一定程度上代表了灌区的作物耗水量。因此,灌区作物耗水在灌溉期和非灌溉期的影响作用有一定的差异。(2)在时间上,风速与实际蒸散发为负相关关系且呈显著负相关(P<0.05),其余影响因素与实际蒸散发均为正相关关系且呈显著正相关(P<0.05);在空间上,除风速与实际蒸散发在大部分区域呈负相关关系,其余影响因素都与实际蒸散发在大部分区域呈正相关关系。因此,除风速外,其余影响因素对灌区作物耗水在大部分区域都为正向促进作用。

基于路面等级识别的车辆半主动悬架内外环控制

针对车辆在不同路面等级下对悬架动态性能与馈能特性需求不同的问题,提出一种基于RF-XGBoost路面等级识别算法的半主动悬架内外环控制策略。利用随机森林(Random Forest,RF)模型对极端梯度提升(Extreme Gradient Boosting,XGBoost)算法进行优化,搭建RF-XGBoost算法模型对路面等级进行识别。将路面等级与悬架控制策略相结合,设计外环为天地棚控制,内环为自适应滑模控制的内外环控制,实现非线性悬架的自适应控制。仿真结果表明,相比传统混合天地棚控制的悬架,内外环控制下的悬架在A级路面下簧载质量加速度降低15.52%,并实现50.4 W的振动能量回收,在B、C级路面下簧载质量加速度分别降低15.09%、16.72%,轮胎动载荷分别降低11.63%、11.42%,在D级路面下轮胎动载荷降低14.12%。台架试验的结果与仿真分析的结果基本一致,表明所设计的自适应内外环控制有效。