Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

Leaf area index （LAI） is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests （RF） method aggregates an ensemble of deci- sion trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined： the Variable Importance Value method and the principal component analysis （PCA） method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error （RMSE） of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable re- duction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the vari- ables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.

Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course(MOOC)has become a popular way of online learning used across the world by millions of people.Meanwhile,a vast amount of information has been collected from the MOOC learners and institutions.Based on the educational data,a lot of researches have been investigated for the prediction of the MOOC learner’s final grade.However,there are still two problems in this research field.The first problem is how to select the most proper features to improve the prediction accuracy,and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data.In order to solve these two problems,an improved random forests method is proposed in this paper.First,a hybrid indicator is defined to measure the importance of the features,and a rule is further established for the feature selection;then,a Clustering-Synthetic Minority Over-sampling Technique(SMOTE)is embedded into the traditional random forests algorithm to solve the class imbalance problem.In experiment part,we verify the performance of the proposed method by using the Canvas Network Person-Course(CNPC)dataset.Furthermore,four well-known prediction methods have been applied for comparison,where the superiority of our method has been proved.

新型城镇化对共同富裕的影响研究——基于Random Forests模型和Loess模型的分析

基于2010—2021年31个省份的相关数据,运用综合评分法测度我国的新型城镇化水平和共同富裕程度,结合Random Forests模型和Loess模型分析新型城镇化对共同富裕的影响效应及其成因。研究发现,新型城镇化总体上可以促进共同富裕,但存在维度异质性与空间异质性。各维度对共同富裕的影响力从高至低排序依次为经济城镇化、人口城镇化、社会城镇化和生态城镇化,各区域按相关性从大到小排序依次为东北、东部、中部和西部地区。不同维度的城镇化对不同区域的共同富裕影响程度不同,对东部、中部和东北地区影响最大的是经济城镇化,对西部地区影响最大的是人口城镇化。基于此,提出以人的全面发展为目标、以经济城镇化为重点、以差异化发展策略为前提、以公共服务均等化为突破口推进共同富裕的政策建议。

Modelling Key Population Attrition in the HIV and AIDS Programme in Kenya Using Random Survival Forests with Synthetic Minority Oversampling Technique-Nominal Continuous

HIV and AIDS has continued to be a major public health concern, and hence one of the epidemics that the world resolved to end by 2030 as highlighted in sustainable development goals (SDGs). A colossal amount of effort has been taken to reduce new HIV infections, but there are still a significant number of new infections reported. HIV prevalence is more skewed towards the key population who include female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID). The study design was retrospective and focused on key population enrolled in a comprehensive HIV and AIDS programme by the Kenya Red Cross Society from July 2019 to June 2021. Individuals who were either lost to follow up, defaulted (dropped out, transferred out, or relocated) or died were classified as attrition;while those who were active and alive by the end of the study were classified as retention. The study used density analysis to determine the spatial differences of key population attrition in the 19 targeted counties, and used Kilifi county as an example to map attrition cases in smaller administrative areas (sub-county level). The study used synthetic minority oversampling technique-nominal continuous (SMOTE-NC) to balance the datasets since the cases of attrition were much less than retention. The random survival forests model was then fitted to the balanced dataset. The model correctly identified attrition cases using the predicted ensemble mortality and their survival time using the estimated Kaplan-Meier survival function. The predictive performance of the model was strong and way better than random chance with concordance indices greater than 0.75.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

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.

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function,leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy.In this case,by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory,a random forest(RF)model is presented to enhance the computing efficiency of reliability degree;moreover,by embedding the RF model into multilevel optimization model,an efficient RF-assisted fatigue reliability-based design optimization framework is developed.Regarding the low-cycle fatigue reliability-based design optimization of aeroengine turbine disc as a case,the effectiveness of the presented framework is validated.The reliabilitybased design optimization results exhibit that the proposed framework holds high computing accuracy and computing efficiency.The current efforts shed a light on the theory/method development of reliability-based design optimization of complex engineering structures.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Learning random forests for ranking

The random forests （RF） algorithm, which combines the predictions from an ensemble of random trees, has achieved significant improvements in terms of classification accuracy. In many real-world applications, however, ranking is often required in order to make optimal decisions. Thus, we focus our attention on the ranking performance of RF in this paper. Our experi- mental results based on the entire 36 UC Irvine Machine Learning Repository （UCI） data sets published on the main website of Weka platform show that RF doesn＇t perform well in ranking, and is even about the same as a single C4.4 tree. This fact raises the question of whether several improvements to RF can scale up its ranking performance. To answer this question, we single out an improved random forests （IRF） algorithm. Instead of the information gain measure and the maximum-likelihood estimate, the average gain measure and the similarity- weighted estimate are used in IRF. Our experiments show that IRF significantly outperforms all the other algorithms used to compare in terms of ranking while maintains the high classification accuracy characterizing RF.

Variable importance-weighted Random Forests

Background： Random Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest. Methods： We propose variable importance-weighted Random Forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split from the randomly selected features. Results： We evaluate the performance of our method through comprehensive simulation and real data analyses, for both regression and classification. Compared to the standard Random Forests and the feature elimination Random Forests methods, our proposed method has improved performance in most cases. Conclusions： By incorporating the variable importance scores into the random feature selection step, our method can better utilize more informative features without completely ignoring less informative ones, hence has improved prediction accuracy in the presence of weak signals and large noises. We have implemented an R package ＂viRandomForests＂ based on the original R package ＂randomForest＂ and it can be freely downloaded from http：// zhaocenter.org/software.

Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data

Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples.Although many studies have investigated this problem,there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples.Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries,we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and as-sembly approaches to obtain the relative abundance profiles of both known and novel genomes.The random forests(RF)classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance profiles.Based on within data cross-validation and cross-dataset prediction,we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and Bracken.We also develop a novel method to integrate the relative abundance profiles of both known and novel microbial or-ganisms to further increase the prediction performance for colorectal cancer from metagenomes.

Multiple Random Forests Based Intelligent Location of Single-phase Grounding Fault in Power Lines of DFIG-based Wind Farm

To address the problems of wind power abandonment and the stoppage of electricity transmission caused by a short circuit in a power line of a doubly-fed induction generator(DFIG) based wind farm, this paper proposes an intelligent location method for a single-phase grounding fault based on a multiple random forests(multi-RF) algorithm. First, the simulation model is built, and the fundamental amplitudes of the zerosequence currents are extracted by a fast Fourier transform(FFT) to construct the feature set. Then, the random forest classification algorithm is applied to establish the fault section locator. The model is resampled on the basis of the bootstrap method to generate multiple sample subsets, which are used to establish multiple classification and regression tree(CART) classifiers. The CART classifiers use the mean decrease in the node impurity as the feature importance,which is used to mine the relationship between features and fault sections. Subsequently, a fault section is identified by voting on the test results for each classifier. Finally, a multi-RF regression fault locator is built to output the predicted fault distance. Experimental results with PSCAD/EMTDC software show that the proposed method can overcome the shortcomings of a single RF and has the advantage of locating a short hybrid overhead/cable line with multiple branches. Compared with support vector machines(SVMs)and previously reported methods, the proposed method can meet the location accuracy and efficiency requirements of a DFIG-based wind farm better.

A new correlation-based approach for ensemble selection in random forests

Purpose-Ensemble methods have been widely used in the field of pattern recognition due to the difficulty offinding a single classifier that performs well on a wide variety of problems.Despite the effectiveness of thesetechniques,studies have shown that ensemble methods generate a large number of hypotheses and thatcontain redundant classifiers in most cases.Several works proposed in the state of the art attempt to reduce allhypotheses without affecting performance.Design/methodology/approach-In this work,the authors are proposing a pruning method that takes intoconsideration the correlation between classifiers/classes and each classifier with the rest of the set.The authorshave used the random forest algorithm as trees-based ensemble classifiers and the pruning was made by atechnique inspired by the CFS(correlation feature selection)algorithm.Findings-The proposed method CES(correlation-based Ensemble Selection)was evaluated onten datasets from the UCI machine learning repository,and the performances were compared to sixensemble pruning techniques.The results showed that our proposed pruning method selects a smallensemble in a smaller amount of time while improving classification rates compared to the state-of-the-artmethods.Originality/value-CES is a new ordering-based method that uses the CFS algorithm.CES selects,in a shorttime,a small sub-ensemble that outperforms results obtained from the whole forest and the other state-of-thearttechniques used in this study.

Data cleaning method for the process of acid production with flue gas based on improved random forest

Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the operating environment of acid production with flue gas is complex and there is much equipment.The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers.Therefore,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is proposed.Firstly,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset.Secondly,an improved random forest regression model is established.Genetic algorithm is used to optimize the hyperparameters of the random forest regression model.Then the optimal parameter combination is found in the search space and the trend of data is predicted.Finally,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized.Results show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue gas.The method improves the accuracy of compensation for missing data.With the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature control.The conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.

Research on stock trend prediction method based on optimized random forest

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis.Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock trend.This study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction.This study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random forest.As the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as input.Then,the parameter combination of the model is optimized through random parameter search.The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine model.Combined with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Interpreting uninterpretable predictors:kernel methods,Shtarkov solutions,and random forests

Many of the best predictors for complex problems are typically regarded as hard to interpret physically.These include kernel methods,Shtarkov solutions,and random forests.We show that,despite the inability to interpret these three predictors to infinite precision,they can be asymptotically approximated and admit conceptual interpretations in terms of their mathe-matical/statistical properties.The resulting expressions can be in terms of polynomials,basis elements,or other functions that an analyst may regard as interpretable.

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.