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Random forest algorithm reveals novel sites in HA protein that shift receptor binding preference of the H9N2 avian influenza virus 认领 引用 被引量：2
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作者 Yuncong Yin Wen Li +7 位作者 Rujian Chen Xiao Wang Yiting Chen Xinyuan Cui Xingbang Lu David M.Irwin Xuejuan Shen Yongyi Shen 《Virologica Sinica》 SCIE CAS CSCD 2025年第1期109-117,共9页
A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,... A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV. 展开更多
关键词 H9N2 Hemagglutinin(HA) Receptor binding preference Random forest algorithm Host shift Interspecies transmission
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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm 认领 引用 被引量：11
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作者 Tie Yan Rui Xu +2 位作者 Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期1135-1148,共14页
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 ... 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. 展开更多
关键词 Intelligent drilling Closed-loop drilling Lithology identification Random forest algorithm Feature extraction
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Object-based classification of hyperspectral data using Random Forest algorithm 认领 引用 被引量：8
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作者 Saeid Amini Saeid Homayouni +1 位作者 Abdolreza Safari Ali A.Darvishsefat 《Geo-Spatial Information Science》 SCIE EI CSCD 2018年第2期127-138,共12页
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)algori... 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. 展开更多
关键词 Object-based classification Random Forest algorithm multi-resolution segmentation(MRS) hyperspectral imagery
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Investigation of Nuclear Binding Energy and Charge Radius Based on Random Forest Algorithm 认领 引用 被引量：1
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作者 CAI Boshuai YU Tianjun +3 位作者 LIN Xuan ZHANG Jilong WANG Zhixuan YUAN Cenxi 《原子能科学技术》 EI CAS CSCD 北大核心 2023年第4期704-712,共9页
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 ... 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. 展开更多
关键词 nuclear binding energy nuclear charge radius random forest algorithm
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Random forest algorithm and regional applications of spectral inversion model for estimating canopy nitrogen concentration in rice 认领 引用 被引量：26
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作者 LI Xuqing LIU Xiangnan LIU Meiling WU Ling 《遥感学报》 CSCD 北大核心 2014年第4期923-945,共23页
Canopy Nitrogen Concentration （CNC） is a key indicator of crop yields. It is feasible to establish a real- time regional model to estimate CNC by upscaling the field-scale spectral model. This study focuses on monit... Canopy Nitrogen Concentration （CNC） is a key indicator of crop yields. It is feasible to establish a real- time regional model to estimate CNC by upscaling the field-scale spectral model. This study focuses on monitoring the CNC in rice on a large scale in real-time. The Random Forest （RF） algorithm is used to establish the CNC spectral inversion model, and some vegetation indexes that are sensitive to nitrogen were selected as input parameters for the RF. CNC was selected as an output parameter. The hyperspectral and biochemical data were collected in a paddy in Changchun City, Jilin Province, China, and the data in Suzhou was used to test the model＇s universality and effectiveness. Two regional-scale models were developed by applying scale transformation based on the input and output variables respectively. The results show that the RFCNC model （CNC spectral inversion model based on the RF algorithm） performed accurately and significantly improved upon existing methods. R2, used to validate method accuracy in Changchun and Suzhou, was 0.82 and 0.73 respectively. The regional application accuracy increased （R2= 0. 81） through the two upscaling methods using hyperspectral remote sensing satellite images. This study suggests that this method is promising for estimating regional CNC in rice by upscaling a field-scale spectral model if the strategy is appropriately selected. 展开更多
关键词 random forest algorithm, nitrogen inversion model, upscaling, regional application
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Improved Random Forest Algorithm Based on Adaptive Step Size Artificial Bee Colony Optimization 认领 引用
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作者 Jiuyuan Huo Xuan Qin +2 位作者 Hamzah Murad Mohammed Al-Neshmi Lin Mu Tao Ju 《国际计算机前沿大会会议论文集》 EI 2020年第2期216-233,共18页
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 ... 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. 展开更多
关键词 Random forest algorithm Artificial bee colony algorithm Unbalanced data Classification problem
Maize crop residue cover mapping using Sentinel-2 MSI data and random forest algorithms 认领 引用 被引量：1
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作者 Jia Du Pierre-Andre Jacinthe +9 位作者 Kaishan Song Longlong Zhang Boyu Zhao Hua Liu Yan Wang Weijian Zhang Zhi Zheng Weilin Yu Yiwei Zhanga Dapeng Jiang 《International Soil and Water Conservation Research》 SCIE CAS CSCD 2025年第1期189-202,共14页
The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricul... The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricultural production systems in a region.Recognizing the limitations of traditional CRC methods,a new method is proposed for estimating the spatial and temporal distribution of maize residue cover(MRC)in the Jilin Province,NE China.The method used random forest(RF)algorithms,13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data.The tillage indices with the best predictive performance were STI and NDTI(R2 of 0.85 and 0.84,respectively).Among the texture features,the bestfitting was Band8AMean-5*5(R2 of 0.56 and 0.54 for the line-transect and photographic methods,respectively).Based on MSE and InNodePurity,the optimal combination of RF algorithm for the linetransect method was STI,NDTI,NDI7,NDRI5,SRNDI,NDRI6,NDRI7 and Band3Mean-3*3.Likewise,the optimal combination of RF algorithm for the photographic method was STI,NDTI,NDI7,SRNDI,NDRI6,NDRI5,NDRI9 and Band3Mean-3*3.Regional distribution of MRC in the Jilin Province,estimated using the RF prediction model,was higher in the central and southeast sections than in the northwest.That distribution was in line with the spatial heterogeneity of maize yield in the region.These findings showed that the RF algorithm can be used to map regional MRC and,therefore,represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices. 展开更多
关键词 Random forest algorithm Maize residue cover Sentinel-2 remotely sensed data Line-transect method Photographic method
Prostate cancer prediction forest algorithm that takes using the random into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen 认领 引用 被引量：7
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作者 Li-Hong Xiao Pei-Ran Chen +4 位作者 Zhong-Ping Gou Yong-Zhong Li Mei Li Liang-Cheng Xiang Ping Feng 《Asian Journal of Andrology》 SCIE CAS CSCD 2017年第5期586-590,共5页
The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. ... The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels （P 〈 0.001）, as well as in all transrectal ultrasound characteristics （P 〈 0.05） except uneven echo （P = 0.609）. The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy. 展开更多
关键词 diagnosis prostate cancer prostate-specific antigen random forest algorithm transrectal ultrasound characteristics
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Enhancing rock slope stability prediction using random forest machine learning:A case study 认领 引用
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作者 Afiqah Ismail Ahmad Safuan A Rashid +10 位作者 Ali Dehghanbanadaki Rafiuddin Hakim Roslan Mohd Firdaus Md Dan@Azlan Abd Wahid Rasib Radzuan Saari Mushairry Mustaffar Azman Kassim Rini Asnida Abdullah Khairul Hazman Padil Norbazlan Mohd Yusof Norisam Abd Rahaman 《China Geology》 CAS CSCD 2025年第4期691-706,共16页
The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(... The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope. 展开更多
关键词 Slope stability prediction Random Forest Algorithm Remote sensing in Geology Factor of Safety(FOS) Geometrical parameters Rock quality designation(RQD) Multilayer perceptron(MLP)
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Enhanced diagnosis of diabetic retinopathy:integrating advanced algorithms for automated detection and classification 认领 引用
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作者 E Murali T Prasad +2 位作者 JHari Krishna Abdul Hussain Sharief Peta Nandini 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第4期637-645,共9页
AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learni... AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learning techniques.METHODS:Fundus images from the IDRiD dataset and additional Kaggle datasets were used.A wavelet-based band-pass filter was applied for edge enhancement of retinal features.Gaussian mixture model(GMM)clustering was used to segment and extract texture features.These extracted features were classified using machine learning algorithms,including a random forest classifier and a multilayer perceptron neural network.Performance metrics such as sensitivity,specificity,and accuracy were computed to evaluate the proposed model’s diagnostic effectiveness.RESULTS:The random forest-based classification system achieved a sensitivity of 95.08%,specificity of 86.67%,and overall accuracy of 95.20%in detecting DR lesions.The combination of wavelet-based edge enhancement,GMM clustering,and neural network-based feature classification demonstrated high reliability in lesion identification.CONCLUSION:The proposed method effectively detects early signs of DR from fundus images,offering a highaccuracy,automated,and scalable solution for assisting ophthalmologists.Its application can support large-scale screening programs,particularly in regions with limited access to specialized eye care. 展开更多
关键词 diabetic retinopathy random forest algorithm Gaussian mixture modelclustering multilayer perceptron fundus image analysis
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Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China 认领 引用 被引量：1
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作者 SHI Xiaoliang CHEN Jiajun +2 位作者 DING Hao YANG Yuanqi ZHANG Yan 《Chinese Geographical Science》 SCIE CSCD 2024年第2期342-356,共15页
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 r... 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)(R2=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 R2of 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)(R2=0.680)and normal year(2017)(R2=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R2=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield. 展开更多
关键词 winter wheat yield estimation sparrow search algorithm combined with random forest(SSA-RF) machine learning multi-source indicator optimal lead time Henan Province,China
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Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review 认领 引用 被引量：18
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作者 Ernest Yeboah Boateng Joseph Otoo Daniel A. Abaye 《Journal of Data Analysis and Information Processing》 2020年第4期341-357,共17页
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 (... 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. 展开更多
关键词 Classification Algorithms Non-Parametric K-Nearest-Neighbor Neural Networks Random Forest Support Vector Machines
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Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China 认领 引用 被引量：9
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作者 Huiling Tian Jianhua Zhu +8 位作者 Xiao He Xinyun Chen Zunji Jian Chenyu Li Qiangxin Ou Qi Li Guosheng Huang Changfu Liu Wenfa Xiao 《Forest Ecosystems》 SCIE CSCD 2022年第3期396-406,共11页
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 diff... 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(R2=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R2=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. 展开更多
关键词 Stand volume growth Stand origin Plant functional type National forest inventory data Random forest algorithms
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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 认领 引用
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作者 Gilson Tekendo Djoukoue Idriss Djiofack Teledjieu Sijun Bai 《Journal of Management Science & Engineering Research》 2023年第2期60-81,共22页
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 medi... 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. 展开更多
关键词 Electrical and electronic equipment(EEE) Waste from electrical and electronic equipment(WEEE) General equilibrium theory Random forest regression algorithm Decision-making Cameroon
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Impacts of random negative training datasets on machine learning-based geologic hazard susceptibility assessment 认领 引用 被引量：2
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作者 Hao Cheng Wei Hong +3 位作者 Zhen-kai Zhang Zeng-lin Hong Zi-yao Wang Yu-xuan Dong 《China Geology》 CAS CSCD 2025年第4期676-690,共15页
This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,... This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures. 展开更多
关键词 Landslides Debris flows Collapses Ground fissures Geologic hazard prevention and control engineering Geologic hazard susceptibility assessment Negative training dataset Average spatial correlation Random forest algorithm Risk and return analysis Geological survey engineering Loess Plateau area
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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 认领 引用 被引量：11
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作者 杨永娇 肖建毅 +1 位作者 赵创业 周开东 《计算机与现代化》 2020年第3期99-102,126,共4页
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 For... 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。 展开更多
关键词 Isolation Forest算法 Random Forest算法 异常检测算法 时间序列数据 智能电网
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Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon 认领 引用
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作者 Cauan Ferreira Araújo Raimundo Cosme de Oliveira Junior Troy Patrick Beldini 《Journal of Geographic Information System》 2021年第4期434-451,共18页
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 geom... 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 Ch&#227;o 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. 展开更多
关键词 Digital Soil Mapping Upscaling Machine Learning Random Forest Algorithm Multiscale Geomorphometric Generalization
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定量评估气象条件对滇池蓝藻水华发生的影响及预测 认领 引用 被引量：4
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作者 徐虹 戴丛蕊 +2 位作者 何雨芩 程晋昕 王玉尤婷 《水生态学杂志》 CAS CSCD 北大核心 2026年第2期89-96,共8页
对滇池蓝藻水华发生的可能性进行预测,为预防和开展藻华防治、保护水环境提供科学依据。基于2001―2021年逐日MODIS数据和随机森林算法,分别构建复苏期(3―6月)和高发期(7―12月)滇池蓝藻水华发生气象概率预测模型,并采用特征变量重要... 对滇池蓝藻水华发生的可能性进行预测,为预防和开展藻华防治、保护水环境提供科学依据。基于2001―2021年逐日MODIS数据和随机森林算法,分别构建复苏期(3―6月)和高发期(7―12月)滇池蓝藻水华发生气象概率预测模型,并采用特征变量重要性和偏依赖图定量评估了水华发生与气象因子之间的关系。结果表明:(1)近21年滇池蓝藻水华发生年累计频次和规模的均值分别为26.9次和7.30%,水华发生有明显的季节性特征。(2)影响水华发生的关键气象因子在复苏期为气温和风速,气温对水华发生的影响大于风速;高发期则为气温、风速、日照和降水,其中风速的影响最大,其次是气温,日照和降水的影响最小。(3)总体上,气温和降水会加剧蓝藻水华的发生,风速和日照则有抑制作用;气温、光照和降水对水华发生的影响具有一定的累积效应。(4)各因子对蓝藻水华的影响存在一定的适宜区间,超出或低于相应的区间可能会不利于水华的发生;当气温>18℃和风速<2.5 m/s时,发生水华的概率相对较高。(5)模型在复苏期的准确率、召回率、综合评价得分和受试者工作曲线下的面积值分别为80.1%、62.3%、63.4%和87.6%,而高发期为83.1%、85.2%、88.8%和86.0%。 展开更多
关键词 蓝藻水华 气象条件 出现概率 随机森林 滇池
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机器学习驱动的滑坡易发性多模型分级优化研究——以四川省理县为例 认领 引用 被引量：3
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作者 应欣翰 吴彩燕 +3 位作者 贾应 蒙齐 马世乾 廖军 《现代地质》 CAS CSCD 北大核心 2026年第1期263-274,共12页
滑坡易发性评价是地质灾害防治的重要基础工作,对灾害风险防控具有关键作用。然而,传统分级方法在处理边界模糊数据时存在明显不足。为此,本文以四川省理县为研究区,提出一种基于聚类算法的滑坡易发性分级方法。首先采用随机森林模型(Ra... 滑坡易发性评价是地质灾害防治的重要基础工作,对灾害风险防控具有关键作用。然而,传统分级方法在处理边界模糊数据时存在明显不足。为此,本文以四川省理县为研究区,提出一种基于聚类算法的滑坡易发性分级方法。首先采用随机森林模型(Random Forest,RF)计算易发性指数,进而应用层次聚类法(Hierarchical Clustering,HC)和高斯混合模型法(Gaussian Mixture Model,GMM)进行分级,并与传统等间隔划分法和自然断点法进行对比。研究结果表明,聚类算法(尤其是层次聚类法)在处理分布不均、边界模糊的易发性指数时具有更高精度,其中层次聚类法所划定的极高易发区包含了96.1%的历史滑坡点,频率比达3.76,性能最优。实地验证进一步表明,该方法识别出的极高易发区与实际灾害分布高度吻合,证明了其在实际应用中的有效性和可靠性。 展开更多
关键词 滑坡易发性 机器学习 随机森林 聚类算法 指数分级
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基于机器学习的中国农业净碳汇预测模型构建及驱动因素响应分析 认领 引用 被引量：4
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作者 唐湘博 黄有为 苏涵 《环境科学》 EI CAS CSCD 北大核心 2026年第6期3556-3572,共17页
农业净碳汇的智能化预测及驱动因素的机制研究对推进“双碳”目标下我国农业农村减排固碳政策的实施具有重要意义.基于2000~2022年中国31个省域的农业净碳汇的测算数据和面板数据,运用多种机器学习算法构建农业净碳汇预测模型;并通过SHA... 农业净碳汇的智能化预测及驱动因素的机制研究对推进“双碳”目标下我国农业农村减排固碳政策的实施具有重要意义.基于2000~2022年中国31个省域的农业净碳汇的测算数据和面板数据,运用多种机器学习算法构建农业净碳汇预测模型;并通过SHAP值和PDP图揭示该预测模型驱动因素对农业净碳汇的响应特征.结果表明:(1)研究构建的GWO-RF模型对农业碳净汇的预测精度高且稳定性好(MSE=0.04%,MAPE=7%,R2=0.984).(2)模型驱动因素的重要性排序为:农田有效灌溉面积>耕地面积>省域特征>化肥施用强度>地区受教育水平>城镇化水平>农业机械化水平.(3)农田有效灌溉面积、耕地面积和化肥施用强度这3个重要驱动因素两两组合对农业净碳汇交互影响的2D PDP结果显示:其一,农田有效灌溉面积与耕地面积组合的交互作用以农田有效灌溉面积1 600×10~3 hm2为界,小于该值交互作用较弱,大于该值则较强;其二,农田有效灌溉面积与化肥施用强度组合的交互作用较弱,且以农田有效灌溉面积的影响为主导;其三,耕地面积与化肥施用强度组合的交互作用以耕地面积1 600×10~3 hm2为界,小于该值交互作用较弱,大于该值则较强.(4)上述3个驱动因素组合对农业净碳汇共同影响的3D PDP结果显示:农田有效灌溉面积对农业净碳汇的影响最为显著,几乎主导农业净碳汇变化的整个进程,其深层次原因是灌溉强度和灌溉方式均可较大程度地影响作物碳吸收和土壤碳汇的潜能.研究成果将为农业净碳汇的预测提供一种新方法和新思路,也可为政府和相关管理部门制定农业碳减排增汇规划和政策提供决策参考. 展开更多
关键词 农业净碳汇 预测模型 灰狼优化-随机森林算法(GWO-RF) ISM算法 Lasso回归
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