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Prediction method of restoring force based on online AdaBoost regression tree algorithm in hybrid test 认领 引用 被引量：1
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作者 Wang Yanhua Lü Jing +1 位作者 Wu Jing Wang Cheng 《Journal of Southeast University(English Edition)》 EI CAS 2020年第2期181-187,共7页
In order to solve the poor generalization ability of the back-propagation(BP)neural network in the model updating hybrid test,a novel method called the AdaBoost regression tree algorithm is introduced into the model u... In order to solve the poor generalization ability of the back-propagation(BP)neural network in the model updating hybrid test,a novel method called the AdaBoost regression tree algorithm is introduced into the model updating procedure in hybrid tests.During the learning phase,the regression tree is selected as a weak regression model to be trained,and then multiple trained weak regression models are integrated into a strong regression model.Finally,the training results are generated through voting by all the selected regression models.A 2-DOF nonlinear structure was numerically simulated by utilizing the online AdaBoost regression tree algorithm and the BP neural network algorithm as a contrast.The results show that the prediction accuracy of the online AdaBoost regression algorithm is 48.3%higher than that of the BP neural network algorithm,which verifies that the online AdaBoost regression tree algorithm has better generalization ability compared to the BP neural network algorithm.Furthermore,it can effectively eliminate the influence of weight initialization and improve the prediction accuracy of the restoring force in hybrid tests. 展开更多
关键词 hybrid test restoring force prediction generalization ability AdaBoost regression tree
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Groundwater level prediction of landslide based on classification and regression tree 认领 引用 被引量：3
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作者 Yannan Zhao Yuan Li +1 位作者 Lifen Zhang Qiuliang Wang 《Geodesy and Geodynamics》 2016年第5期348-355,共8页
According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the chang... According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides. 展开更多
关键词 Landslide Groundwater level Prediction Classification and regression tree Three Gorges Reservoir area
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Mapping species assemblages of tropical forests at different hierarchical levels based on multivariate regression trees 认领 引用
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作者 Qi Yang Maaike Y.Bader +3 位作者 Guang Feng Jialing Li Dexu Zhang Wenxing Long 《Forest Ecosystems》 SCIE CSCD 2023年第3期387-397,共11页
Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species divers... Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.Methods: We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve(WNNR), Hainan Island,China, using multivariate regression trees(MRT). The MRT was based on arboreal vegetation composition in 132plots of 20 m×20 m with a regular spacing of 1 km. Apart from the MRT, non-metric multidimensional scaling(NMDS) was used to evaluate vegetation-environment relationships.Results: The MRT model worked best when using 14 key environmental variables including topography, climate,latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil(especially available P), together with topographic variables, all influenced the distribution of formations and associations.Conclusions: While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests. 展开更多
关键词 Species assemblages Tropical forest Mapping Multivariate regression trees Non-metric multidimensional scaling
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A New Approach to Predict Financial Failure: Classification and Regression Trees (CART) 认领 引用 被引量：1
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作者 Ayse Guel Yllgoer UEmit Dogrul Guelhan Orekici Temel 《Journal of Modern Accounting and Auditing》 2011年第4期329-339,共11页
The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more ... The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure. 展开更多
关键词 business failure financial distress prediction classification and regression trees （CART）
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Segmented Linear Regression Trees 认领 引用
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作者 Xiangyu Zheng Songxi Chen 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2025年第2期498-521,共24页
Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-... Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees(SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression(SLR) models,which is much more efficient and flexible than the traditional grid search method. Along this way,we propose to use the conditional Kendall's τ correlation coefficient to select the underlying change points. From the non-parametric view, we propose an efficient greedy splitting method that selects the splits by analyzing the association between residuals and each candidate split variable. Further, with the SLRT as a single-tree predictor, we propose a linear random forest approach that aggregates the SLRTs by a weighted average. Both simulation and empirical studies showed significant improvements than the CART trees and even the random forest. 展开更多
关键词 Regression trees segmented linear regression split selection tree pruning
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Services on Academic Achievement: A Robust Estimation Using Bayesian Additive Regression Trees 认领 引用
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作者 Yuntian Zuo 《Open Journal of Statistics》 2025年第6期445-472,共28页
Determining the causal effect of special education is a critical topic when mak-ing educational policy that focuses on student achievement.However,current special education research is facing challenges from persisten... Determining the causal effect of special education is a critical topic when mak-ing educational policy that focuses on student achievement.However,current special education research is facing challenges from persistent selection bias and complex confounding.Bayesian Additive Regression Trees(BART)is em-ployed in this study to provide a flexible estimation of the academic perfor-mance.Targeted Maximum Likelihood Estimation(TMLE)is also integrated into the BART model,supporting doubly robust estimation of the special ed-ucation effect.This study extracted survey data from the Early Childhood Lon-gitudinal Study,Kindergarten Class(ECLS-K),to estimate the causal impact of special education status on students’combined mathematics and reading achievement scores.The analysis results of the BART-TMLE model show that children receiving special education services demonstrated approximately 9 points lower scores on average for combined math and reading scores,even adjusting for a considerable number of covariates,compared to their peers who did not receive these services.The estimated negative treatment effect persists after controlling for observed covariates that are closely correlated to the combined test score.The negative effect likely reflects unobserved factors,such as the underlying severity of learning disabilities,parent involvement and other potential traits,which are actual factors that determine the placement of special education status,rather than indicating the ineffectiveness of special education service.The achievement gap in academic performance reflects the current observable status of special education.The estimated effect could be improved by future research incorporating educational domain knowledge,allowing the model to be constructed more accurately. 展开更多
关键词 Bayesian Additive Regression Trees Targeted Maximum Likelihood Estimation Unmeasured Confounding Heterogeneous Treatment Effects Special Education Causal Impact
Artificial Neural Network(ANN)and Regression Tree(CART)applications for the indirect estimation of unsaturated soil shear strength parameters 认领 引用 被引量：5
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作者 D.P.KANUNGO Shaifaly SHARMA Anindya PAIN 《Frontiers of Earth Science》 SCIE CSCD 2014年第3期439-456,共18页
The shear strength parameters of soil(cohesion and angle of internal friction)are quite essential in solving many civil engineering problems.In order to determine these parameters,laboratory tests are used.The main ob... The shear strength parameters of soil(cohesion and angle of internal friction)are quite essential in solving many civil engineering problems.In order to determine these parameters,laboratory tests are used.The main objective of this work is to evaluate the potential of Artificial Neural Network(ANN)and Regression Tree(CART)techniques for the indirect estimation of these parameters.Four different models,considering different combinations of 6 inputs,such as gravel%,sand%,silt%,clay%,dry density,and plasticity index,were investigated to evaluate the degree of their effects on the prediction of shear parameters.A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures.It was observed that for the prediction of friction angle,the performance of both the techniques is about the same.However,for the prediction of cohesion,the ANN technique performs better than the CART technique.It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters.Also,connection weight and bias analyses of the best neural network(i.e.,6/2/2)were attempted using Connec-tion Weight,Garson,and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters.It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance,and should be favored over the other approaches examined in this study. 展开更多
关键词 cohesion friction angle Artificial NeuralNetwork Regression Tree Connection Weight Weight-bias Approach
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Predicting plant disease epidemics using boosted regression trees 认领 引用
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作者 Chun Peng Xingyue Zhang Weiming Wang 《Infectious Disease Modelling》 CSCD 2024年第4期1138-1146,共9页
Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head b... Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to anthesis.The scalar-on-function models fit the data better than previously described logistic regression models.In this work,given the same dataset and models,we attempt to reproduce the article by Shah et al.using a different approach,boosted regression trees.After fitting,the classification accuracy and model statistics are surprisingly good. 展开更多
关键词 Plant disease epidemics Scalar-on-function model Boosted regression trees
Partially fixed bayesian additive regression trees 认领 引用
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作者 Hao Ran Yang Bai 《Statistical Theory and Related Fields》 CSCD 2024年第3期232-242,共11页
Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain do... Bayesian Additive Regression Trees(BART)is a widely popular nonparametric regression model known for its accurate prediction capabilities.In certain situations,there is knowledge suggesting the existence of certain dominant variables.However,the BART model fails to fully utilize the knowledge.To tackle this problem,the paper introduces a modification to BART known as the Partially Fixed BART model.By fixing a portion of the trees’structure,this model enables more efficient utilization of prior knowledge,resulting in enhanced estimation accuracy.Moreover,the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent.Empirical results substantiate the enhancement of the proposed model in comparison to the original BART. 展开更多
关键词 Bayesian additive regression trees nonparametric model machine learning variable importance
Simulation of reservoir outflows using regression tree and support vector machine 认领 引用
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作者 Vijay Kaushik Noopur Awasthi 《AI in Civil Engineering》 2023年第1期93-104,共12页
Water stored in reservoirs has a lot of crucial function,including generating hydropower,supporting water supply,and relieving lasting droughts.During floods,water deliveries from reservoirs must be acceptable,so as t... Water stored in reservoirs has a lot of crucial function,including generating hydropower,supporting water supply,and relieving lasting droughts.During floods,water deliveries from reservoirs must be acceptable,so as to guarantee that the gross volume of water is at a safe level and any release from reservoirs will not trigger flooding downstream.This study aims to develop a well-versed assessment method for managing reservoirs and pre-releasing water outflows by using the machine learning technology.As a new and exciting AI area,this technology is regarded as the most valuable,time-saving,supervised and cost-effective approach.In this study,two data-driven forecasting models,i.e.,Regression Tree(RT)and Support Vector Machine(SVM),were employed for approximately 30 years’hydrological records,so as to simulate reservoir outflows.The SVM and RT models were applied to the data,accurately predicting the fluctuations in the water outflows of a Bhakra reservoir.Different input combinations were used to determine the most effective release.For cross-validation,the number of folds varied.It is found that quadratic SVM for 10 folds with seven different parameters would give the minimum RMSE,maximum R2,and minimum MAE;therefore,it can be considered as the best model for the dataset used in this study. 展开更多
关键词 Reservoir outflow Regression tree Support vector machine Error analysis
干旱区生态系统碳储量时空演变及其驱动机制的非线性响应及阈值效应分析——以鄂尔多斯市为例 认领 引用
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作者 刘轶敏 何洋 +6 位作者 苏瑞景 樊丽 刘诺 吴俊 李纾宜 彭渝胜 郑春丽 《环境科学学报》 CAS CSCD 北大核心 2026年第3期466-480,共15页
我国干旱区碳储量是区域碳库的重要组成,其动态变化直接影响碳循环平衡,然而受人类活动与土壤盐碱化、沙化等影响,干旱区碳储量流失风险骤增,明确其演变规律与驱动机制成为制定生态保护策略、维系碳储量稳定的关键.本研究以我国典型干... 我国干旱区碳储量是区域碳库的重要组成,其动态变化直接影响碳循环平衡,然而受人类活动与土壤盐碱化、沙化等影响,干旱区碳储量流失风险骤增,明确其演变规律与驱动机制成为制定生态保护策略、维系碳储量稳定的关键.本研究以我国典型干旱区鄂尔多斯市为案例地,耦合PLUS、InVEST、Boosted regression trees模型,探究了该地2040年自然发展和生态保护情景下碳储量时空演变规律,并探索了不同驱动因素对碳储量的非线性影响及阈值效应.结果表明:(1)自然发展情景下,研究区耕地、草地面积减少,林地、水域、建设用地与未利用地面积增加;生态保护情景下,林地、草地、建设用地面积增加,耕地、水域、未利用地面积减少;但与自然发展情景相比,建设用地的扩张有所约束.(2)自然发展情景下该地总碳储量为5128.27×105 t,生态保护情景下为5211.13×105 t,前者较2020年减少43.22×105 t,后者较2020年增加39.65×105t,表明碳储量分布与土地利用类型分布关系密切.(3)从影响碳储量的驱动因素贡献度排名来看,影响最高的是土壤盐碱化,其次分别是土壤沙化、NDVI和煤矿开发强度,最低的是人口密度,且它们的变化均与碳储量呈非线性关系.经研究,干旱区生态管理需严守pH=7.8012~8.0727、沙化指数≤1、NDVI≤0.1088以及煤矿开发强度≤0.24%等阈值边界,以维持生态系统的碳汇功能.本研究通过多模型耦合与情景模拟,不仅揭示了干旱区碳储量对关键驱动因子的非线性响应机制,更重要的是为干旱区生态保护与修复提供了精准的阈值管理依据,对维系区域碳平衡、推动干旱区生态文明建设具有重要的理论与实践指导意义. 展开更多
关键词 土地利用 碳储量 PLUS InVEST Boosted regression trees模型 非线性响应 阈值效应 鄂尔多斯
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Iceberg Draft Prediction Using Several Tree-Based Machine Learning Models 认领 引用
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作者 AZIMI Hamed SHIRI Hodjat 《Journal of Ocean University of China》 SCIE CAS CSCD 2025年第5期1269-1288,共20页
The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations... The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations traversing shallow waters,where ice-berg keels may reach the seabed,potentially damaging subsea structures.Consequently,costly and time-intensive iceberg manage-ment operations,such as towing and rerouting,are undertaken to safeguard subsea and offshore infrastructure.This study,therefore,explores the application of extra tree regression(ETR)as a robust solution for estimating iceberg draft,particularly in the preliminary phases of decision-making for iceberg management projects.Nine ETR models were developed using parameters influencing iceberg draft.Subsequent analyses identified the most effective models and significant input variables.Uncertainty analysis revealed that the superior ETR model tended to overestimate iceberg drafts;however,it achieved the highest precision,correlation,and simplicity in estimation.Comparison with decision tree regression,random forest regression,and empirical methods confirmed the superior perfor-mance of ETR in predicting iceberg drafts. 展开更多
关键词 sea-bottom founded structures iceberg draft extra tree regression decision tree regression random forest regression
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Spatiotemporal variations of dust emission in Qaidam basin during the past 40 years 认领 引用
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作者 ShengFei Yang HeQiang Du YaWei Fan 《Research in Cold and Arid Regions》 CAS CSCD 2026年第2期187-196,共10页
The Qaidam Basin,situated on the Qinghai-Tibetan Plateau,ranks among the highest deserts in the world.Dust derived from this basin are carried to neighboring regions by atmospheric circulation,resulting in accelerated... The Qaidam Basin,situated on the Qinghai-Tibetan Plateau,ranks among the highest deserts in the world.Dust derived from this basin are carried to neighboring regions by atmospheric circulation,resulting in accelerated glacier melting,earlier onset and prolonged duration monsoon,and modification of atmospheric temperature structure,thereby influencing climate patterns.Therefore,it is crucial to understand the characteristics of dust emissions in the Qaidam Basin.However,previous studies have often relied on satellite remote sensing data or models that lack validations.Hence,we use a dust emission model validated with extensive measured data from the Qinghai-Tibetan Plateau to simulate the spatiotemporal distribution of dust emissions in the Qaidam Basin from 1982 to 2020.And,the Classification and Regression Trees(CART)machine learning method is used for multi-factor comprehensive analysis to identify the dominant factors affecting dust emissions and to determine the configuration of factors that have the most significant impact.We found that the dust emissions in the Qaidam Basin showed a slightly increasing trend over the past 40 years.The internal variability of dust emissions and external factors such as meteorological conditions and surface characteristics contributed to a notable increase in dust emissions during the early 2000s.Meanwhile,the annual cyclic variation in meteorological conditions is the reason for the higher dust emissions in spring and early summer and lower ones in the other seasons in this region.The spatial distribution of dust emissions exhibits significant variation with altitude,and in the piedmont alluvial fans that locate the transition zones between the mountains and the basins the dust emission rates generally higher than the other places.The surface soil moisture(SM)and the wind speed at 10-m height(WS)are the dominant factors influencing dust emission,and their mechanisms of affecting dust emission are different.SM plays a crucial role in the initial stage of dust lifting,while WS has a continuous and more significant influence throughout the entire dust emission process.Among the combinations of multiple influencing factors,the one where SM is less than or equal to 0.113 mm3/mm3,WS is greater than 5.159 m/s,and air temperature(AT)is greater than-12.488℃is the most conducive to dust emission.We provide new insights into dust emission in the Qaidam region,offering directions for future research to focus on the different mechanisms of various influencing factors in the dust emission process,as well as on the changes in dust emissions under different configurations of multiple factors. 展开更多
关键词 Classification and regression trees Dust emissions Influencing factors Qaidam Basin Spatiotemporal distribution
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Incorporating spatial autocorrelation into soil salinity models:Insights from the Minqin Oasis and its desert–oasis transition zone in Northwest China 认领 引用
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作者 ZHAO Dan YANG Xiya +3 位作者 GAO Yukun PAN Jing YOU Quangang XUE Xian 《Journal of Arid Land》 SCIE CAS CSCD 2026年第7期1135-1158,共24页
Remote sensing-based soil salinity inversion serves as a crucial approach for monitoring and assessment in arid regions.However,most existing models rarely account for the spatial autocorrelation(SAC)of soil salinity,... Remote sensing-based soil salinity inversion serves as a crucial approach for monitoring and assessment in arid regions.However,most existing models rarely account for the spatial autocorrelation(SAC)of soil salinity,which limits both their predictive accuracy and ability to capture spatial patterns.To address this gap,this study investigated the Minqin Oasis and its adjacent desert–oasis transition zone in Northwest China.Based on collected field soil samples and concurrently acquired Landsat-8 OLI remote sensing images in 2024,we incorporated characteristic bands reflecting SAC into conventional spectral indices.Through multi-band combination optimization and comparison of different models'predictive performance,we constructed an optimal soil salinity inversion model for the Minqin Oasis and its adjacent desert–oasis transition zone.The results demonstrated that incorporating SAC of soil salinity markedly improved model performance,with the Gradient Boosting Regression Trees(GBRT)model incorporating SAC(GBRT_SAC)achieving the best accuracy.Compared with the traditional spectral index-based GBRT model,the coefficient of determination(R2)increased by 7.320%,the root mean square error(RMSE)decreased by 20.230%,and the mean absolute percentage error(MAPE)decreased by 121.01%using the GBRT_SAC model.The soil salinity distribution derived from the GBRT_SAC model revealed pronounced spatial heterogeneity,with salinized areas covering approximately 1256.75 km2(36.170%of the total area).Soil salinity was jointly influenced by natural and anthropogenic factors.At the regional scale,soil type and vegetation type emerged as the dominant drivers shaping soil salinity patterns.In contrast,within the oasis interior,soil salinity was primarily driven by groundwater table regulated by irrigation,leading to surface salt accumulation through capillary rise.In the 1000 m desert–oasis transition zone,the explanatory power(q-value)of all environmental factors for spatial variation of soil salinity significantly increased,indicating a sensitive interface where hydrological and aeolian processes interact.Notably,although soil salinity was relatively lower in sandy areas,sand content emerged as the most influential factor in this region(q-value=0.483),effectively serving as a key indicator of the transitional environment.By introducing SAC-based features into soil salinity inversion models,this study provides a robust methodological framework and valuable data to support understanding and management of soil salinization in arid desert–oasis ecotone systems. 展开更多
关键词 soil salinization spatial autocorrelation(SAC) machine learning Gradient Boosting Regression Trees(GBRT) spatial heterogeneity GeoDetector desert–oasis transition zone
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Nonlinear effects and threshold regulation of landscape pattern indices on ecosystem services:a case study of Qilian Mountain National Park 认领 引用
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作者 PEI Ruian MA Binbin +2 位作者 SU Jingjing HOU Xiaohua ZHANG Yike 《Journal of Arid Land》 SCIE CAS CSCD 2026年第6期968-993,共26页
Accelerated global climate change and intensified human activities profoundly alter landscape patterns and ecosystem services(ESs),making the quantitative evaluation of their dynamic interactions essential for advanci... Accelerated global climate change and intensified human activities profoundly alter landscape patterns and ecosystem services(ESs),making the quantitative evaluation of their dynamic interactions essential for advancing regional sustainable development.This study focused on Qilian Mountain National Park and employed FRAGSTATS 4.2 to analyze the landscape pattern evolution from 2000 to 2020.The Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model was used to assess five key ESs:water yield(WY),carbon storage(CS),water quality purification(ND),soil retention(SR),and habitat quality(HQ).Ecosystem service bundles(ESBs)were identified using a self-organizing map(SOM)approach,and nonlinear relationships between landscape pattern indices and ESs were examined using the Boosted Regression Tree(BRT)model combined with spearman correlation and clustered heatmap analyses.The results indicated that the landscape pattern of Qilian Mountain National Park exhibits a clear east to west gradient.The spatiotemporal dynamics of ESs showed divergent trends,with CS and ND consistently improving,whereas WY exhibited pronounced nonlinear fluctuations.ESBs were classified into four types:ESB I(ecosystem transition bundle),ESB II(ecosystem regulation and protection bundle),ESB III(ecosystem degradation and protection bundle),and ESB IV(ecosystem restoration bundle),reflecting a shift from single function dominance toward multifunctional synergies.A nonlinear coupling relationship existed between landscape pattern indices and total ecosystem services(TES),characterized by a notable decline in TES and continued degradation of ES performance and stability.Together,this study provides a robust scientific foundation for developing differentiated zoning management strategies.The findings deliver valuable scientific insights for the management of ESs in the Qilian Mountain National Park and similar mountain ecosystems,while offering a reference for promoting sustainable development in fragile ecological regions worldwide. 展开更多
关键词 landscape pattern indices ecosystem services(ESs) self-organizing map(SOM) Boosting Regression Tree(BRT) Qilian Mountain National Park
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Comparison of decision tree and naive Bayes algorithms in detecting trace residue of gasoline based on gas chromatography-mass spectrometry data 认领 引用
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作者 Gezani Bin Md Ghazi Loong Chuen Lee +1 位作者 Aznor SSamsudin Hukil Sino 《Forensic Sciences Research》 CSCD 2023年第3期249-255,共7页
Fire debris analysis aims to detect and identify any ignitable liquid residues in burnt residues collected at a fire scene.Typically,the burnt residues are analysed using gas chromatography-mass spectrometry(GC-MS)and... Fire debris analysis aims to detect and identify any ignitable liquid residues in burnt residues collected at a fire scene.Typically,the burnt residues are analysed using gas chromatography-mass spectrometry(GC-MS)and are manually interpreted.The interpretation process can be laborious due to the complexity and high dimensionality of the GC-MS data.Therefore,this study aims to compare the potential of classification and regression tree(CART)and naive Bayes(NB)algorithms in analysing the pixel-level GC-MS data of fire debris.The data comprise 14 positive(i.e.fire debris with traces of gasoline)and 24 negative(i.e.fire debris without traces of gasoline)samples.The differences between the positive and negative samples were first inspected based on the mean chromatograms and scores plots of the principal component analysis technique.Then,CART and NB algorithms were independently applied to the GC-MS data.Stratified random resampling was applied to prepare three sets of 200 pairs of training and testing samples(i.e.split ratio of 7:3,8:2,and 9:1)for estimating the prediction accuracies.Although both the positive and negative samples were hardly differentiated based on the mean chromatograms and scores plots of principal component analysis,the respective NB and CART predictive models produced satisfactory performances with the normalized GC-MS data,i.e.majority achieved prediction accuracy>70%.NB consistently outperformed CART based on the prediction accuracies of testing samples and the corresponding risk of overfitting except when evaluated using only 10%of samples.The accuracy of CART was found to be inversely proportional to the number of testing samples;meanwhile,NB demonstrated rather consistent performances across the three split ratios.In conclusion,NB seems to be much better than CART based on the robustness against the number of testing samples and the consistent lower risk of overfitting. 展开更多
关键词 forensic sciences gas chromatography-mass spectrometry forensic fire debris analysis naive Bayes classifier classification and regression tree principal component analysis
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A retinal blood vessel extraction algorithm based on CART decision tree and improved AdaBoost 认领 引用
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作者 DIWU Peng-peng HU Ya-qi 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第1期61-68,共8页
This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) t... This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm. 展开更多
关键词 classification and regression tree (CART) improved adptive boosting (AdaBoost) retinal blood vessel local binary pattern (LBP) texture
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Quantitative analysis of factors driving the variations in snow cover fraction in the Qilian Mountains,China 认领 引用 被引量：1
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作者 JIN Zizhen QIN Xiang +6 位作者 LI Xiaoying ZHAO Qiudong ZHANG Jingtian MA Xinxin WANG Chunlin HE Rui WANG Renjun 《Journal of Arid Land》 SCIE CSCD 2025年第7期888-911,共24页
Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate qua... Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region. 展开更多
关键词 snow cover fraction surface runoff machine learning histogram-based gradient boosting regression tree(HGBRT)model hydrological effects Qinghai-Xizang Plateau
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Evaluating the Predictability of Stock Market Returns via STARX-Tree Models 认领 引用
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作者 Camila Epprecht Alvaro Veiga 《China-USA Business Review》 2012年第1期1-21,共21页
The goal of this paper is to evaluate the predictability of a stock market using the one-step-ahead forecasts of the returns as a signal for automatic trading. The paper presents a proposal of a multiple regime model ... The goal of this paper is to evaluate the predictability of a stock market using the one-step-ahead forecasts of the returns as a signal for automatic trading. The paper presents a proposal of a multiple regime model that combines aspects from STAR models, and decision trees. The resulting model, so-called STARX-Tree, is a regression tree with smooth transition and linear ARX models fitted in the terminal nodes. The methodology was tested on 23 stocks of the U.S. stock market. The forecasting model is evaluated through statistical and financial measures and compared to the random walk model, the naive approach, the neural networks and the linear ARX model. The results pointed out that the STARX-Tree model outperforms the comparative models under the fmancial criterion. 展开更多
关键词 nonlinear models regression tree STARX-Tree heteroscedastic realized volatility automatic trading
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Enhanced extrapolative machine learning for designing high-performance multi-principal-element superalloys 认领 引用
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作者 Qiu-Ling Tao Long-Ke Bao +6 位作者 Jia-Wen Cao Rong-Pei Shi Yi-Lu Zhao Tao Yang Xue Jia Zhi-Fu Yao Xing-Jun Liu 《Rare Metals》 SCIE EI CAS CSCD 2025年第10期7859-7875,共17页
Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen f... Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L12-phase solvus temperature(TL12)and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high TL12values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a TL12of 1267℃and a density of 7.94 g cm-3,outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L12-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δr>5.3 and ΔHmix<-12.8 J mol-1K-1tend to favor higher TL12. 展开更多
关键词 Machine learning for material design Piecewise symbolic regression tree Extrapolation capability Multi-principal-element superalloys
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