Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally co...Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally confined to building materials and furniture.Relatively little research focuses on estimation of human-related VOCs,which have been shown to contribute significantly to indoor air quality,especially in densely-occupied environments.This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom.The time-resolved concentrations of two typical human-related(ozone-related)VOCs in the classroom over a five-day period were analyzed,i.e.,6-methyl-5-hepten-2-one(6-MHO),4-oxopentanal(4-OPA).By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression(RFR),adaptive boosting(Adaboost),gradient boosting regression tree(GBRT),extreme gradient boosting(XGboost),and least squares support vector machine(LSSVM),we find that the LSSVM approach achieves the best performance,by using multi-feature parameters(number of occupants,ozone concentration,temperature,relative humidity)as the input.The LSSVM approach is then used to predict the 4-OPA concentration,with mean absolute percentage error(MAPE)less than 5%,indicating high accuracy.By combining the LSSVM with a kernel density estimation(KDE)method,we further establish an interval prediction model,which can provide uncertainty information and viable option for decision-makers.The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors,making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.展开更多
Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the predi...Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.展开更多
To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before app...To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.展开更多
In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d c...In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.展开更多
This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock s...This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.展开更多
In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithm...In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.展开更多
In this paper,the least square support vector machine(LSSVM)is used to study the safety of a high-speed railway.According to the principle of LSSVM regression prediction,the parameters of the LSSVM are optimized to mo...In this paper,the least square support vector machine(LSSVM)is used to study the safety of a high-speed railway.According to the principle of LSSVM regression prediction,the parameters of the LSSVM are optimized to model the natural disaster early warning of safe operation of a high-speed railway,and the management measures and methods of high-speed railway safety operation under natural disasters are given.The relevant statistical data of China’s high-speed railway are used for training and verification.The experimental results show that the LSSVM can well reflect the nonlinear relationship between the accident rate and the influencing factors,with high simulation accuracy and strong generalization ability,and can effectively predict the natural disasters in the safe operation of a high-speed railway.Moreover,the early warning system can improve the ability of safety operation evaluation and early warning of high-speed railway under natural disasters,realize the development goals of high-speed railway(safety,speed,economic,low-carbon and environmental protection)and provide a theoretical basis and technical support for improving the safety of a high-speed railway.展开更多
The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation meth...The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task.Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost.This work sorts out and collects 60 sets of measured data of highway engineering;establishes an expressway cost index system based on 10 factors,including main route mileage,roadbed width,roadbed earthwork,and number of bridges;and processes the data through principal component analysis(PCA)and hierarchical cluster analysis.Particle swarm optimization(PSO)is used to obtain the optimal parameter combination of the regularization parameter c and the kernel function width coefficientin least squares support vector machine(LSSVM).Results show that the average relative and mean square errors of the PCA-PSO-LSSVM model are 0.79%and 10.01%,respectively.Compared with BP neural networks and unoptimized LSSVM model,the PCA-PSO-LSSVM model has smaller relative errors,better generalization ability,and higher prediction accuracy,thereby providing a new method for highway cost prediction in complex environments.展开更多
Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid mo...Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.展开更多
由于月度负荷的二重趋势特性,其变化呈现出复杂的非线性组合特征,使预测精度一直不能达到令人满意的结果。针对月负荷的二重趋势特性和最小二乘支持向量机(least squares support vector machine,LSSVM)存在的数据输入维数大、训练时间...由于月度负荷的二重趋势特性,其变化呈现出复杂的非线性组合特征,使预测精度一直不能达到令人满意的结果。针对月负荷的二重趋势特性和最小二乘支持向量机(least squares support vector machine,LSSVM)存在的数据输入维数大、训练时间长等缺点,提出一种基于灰色关联度与LSSVM组合的月度负荷预测方法。该方法通过计算灰色关联度来选择训练样本,选取LSSVM进行样本训练;将与待预测月高度相似的历史月负荷作为LSSVM的训练样本输入,剔除了冗余数据,减少了输入维数,提高了预测精度。通过实例验证和结果对比,证明了该方法可显著提高月负荷预测的精度。展开更多
基金supported by the National Natural Science Foundation of China (No.52178062)the Alfred P.Sloan Foundation (No.G-2016-7050)the Opening Fund of State Key Laboratory of Green Building in Western China (LSKF202311).
文摘Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally confined to building materials and furniture.Relatively little research focuses on estimation of human-related VOCs,which have been shown to contribute significantly to indoor air quality,especially in densely-occupied environments.This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom.The time-resolved concentrations of two typical human-related(ozone-related)VOCs in the classroom over a five-day period were analyzed,i.e.,6-methyl-5-hepten-2-one(6-MHO),4-oxopentanal(4-OPA).By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression(RFR),adaptive boosting(Adaboost),gradient boosting regression tree(GBRT),extreme gradient boosting(XGboost),and least squares support vector machine(LSSVM),we find that the LSSVM approach achieves the best performance,by using multi-feature parameters(number of occupants,ozone concentration,temperature,relative humidity)as the input.The LSSVM approach is then used to predict the 4-OPA concentration,with mean absolute percentage error(MAPE)less than 5%,indicating high accuracy.By combining the LSSVM with a kernel density estimation(KDE)method,we further establish an interval prediction model,which can provide uncertainty information and viable option for decision-makers.The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors,making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.
基金supported by National Natural Science Foundation of China(Nos.61662042,62062049)Science and Technology Plan of Gansu Province(Nos.21JR7RA288,21JR7RE174).
文摘Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.
基金Project(51204082)supported by the National Natural Science Foundation of ChinaProject(KKSY201458118)supported by the Talent Cultivation Project of Kuning University of Science and Technology,China
文摘To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.
文摘In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.
文摘This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.
基金This research is sponsored by the Project:“Support of research and development activities of the J.Selye University in the field of Digital Slovakia and creative industry”of the Research&Innovation Operational Programme(ITMS code:NFP313010T504)co-funded by the European Regional Development Fund.
文摘In the present work,a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide.Four different machine learning algorithms of radial basis function,multi-layer perceptron(MLP),artificial neural networks(ANN),least squares support vector machine(LSSVM)and adaptive neuro-fuzzy inference system(ANFIS)are used to model the solubility of different acids in carbon dioxide based on the temperature,pressure,hydrogen number,carbon number,molecular weight,and the dissociation constant of acid.To evaluate the proposed models,different graphical and statistical analyses,along with novel sensitivity analysis,are carried out.The present study proposes an efficient tool for acid solubility estimation in supercritical carbon dioxide,which can be highly beneficial for engineers and chemists to predict operational conditions in industries.
基金supported by grants from the National Natural Science Foundation of China 51178157High-level Project of the Top Six Talents in Jiangsu Province JXQC-021+1 种基金the Key Science and Technology Program in Henan Province 182102310004the Postgraduate Research and Prac-tice Innovation Program of Jiangsu Province KYCX20-0290.
文摘In this paper,the least square support vector machine(LSSVM)is used to study the safety of a high-speed railway.According to the principle of LSSVM regression prediction,the parameters of the LSSVM are optimized to model the natural disaster early warning of safe operation of a high-speed railway,and the management measures and methods of high-speed railway safety operation under natural disasters are given.The relevant statistical data of China’s high-speed railway are used for training and verification.The experimental results show that the LSSVM can well reflect the nonlinear relationship between the accident rate and the influencing factors,with high simulation accuracy and strong generalization ability,and can effectively predict the natural disasters in the safe operation of a high-speed railway.Moreover,the early warning system can improve the ability of safety operation evaluation and early warning of high-speed railway under natural disasters,realize the development goals of high-speed railway(safety,speed,economic,low-carbon and environmental protection)and provide a theoretical basis and technical support for improving the safety of a high-speed railway.
文摘The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task.Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost.This work sorts out and collects 60 sets of measured data of highway engineering;establishes an expressway cost index system based on 10 factors,including main route mileage,roadbed width,roadbed earthwork,and number of bridges;and processes the data through principal component analysis(PCA)and hierarchical cluster analysis.Particle swarm optimization(PSO)is used to obtain the optimal parameter combination of the regularization parameter c and the kernel function width coefficientin least squares support vector machine(LSSVM).Results show that the average relative and mean square errors of the PCA-PSO-LSSVM model are 0.79%and 10.01%,respectively.Compared with BP neural networks and unoptimized LSSVM model,the PCA-PSO-LSSVM model has smaller relative errors,better generalization ability,and higher prediction accuracy,thereby providing a new method for highway cost prediction in complex environments.
基金National Social Science Foundation of China(No.18AGL028)Social Science Foundation of the Higher Education Institutions Jiangsu Province,China(No.2018SJZDI070)Social Science Foundation of the Jiangsu Province,China(Nos.16ZZB004,17ZTB005)
文摘Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.
文摘负荷历史数据是进行中长期负荷预测的基础。历史数据异常及缺失将严重影响负荷预测模型的精度及有效性。针对传统异常数据辨识方法和缺失数据填补方法的不足,提出了基于T2椭圆图的异常数据识别和基于最小二乘支持向量机(least square support vector machine,LSSVM)的缺失数据填补方法。采用偏最小二乘法(partial least square,PLS)提取历史数据主成份,计算各历史样本对主成份的累积贡献率(accumulative contribution rate,ACR),并绘制T2椭圆,从而识别出历史样本贡献率过大的异常数据。用最小二乘支持向量机拟合历史数据变化趋势,从而实现缺失数据的填补。算例结果表明:T2椭圆图能有效识别历史数据中的异常样本;最小二乘支持向量机具有良好的数据填补特性,具有较强的实用价值。
文摘由于月度负荷的二重趋势特性,其变化呈现出复杂的非线性组合特征,使预测精度一直不能达到令人满意的结果。针对月负荷的二重趋势特性和最小二乘支持向量机(least squares support vector machine,LSSVM)存在的数据输入维数大、训练时间长等缺点,提出一种基于灰色关联度与LSSVM组合的月度负荷预测方法。该方法通过计算灰色关联度来选择训练样本,选取LSSVM进行样本训练;将与待预测月高度相似的历史月负荷作为LSSVM的训练样本输入,剔除了冗余数据,减少了输入维数,提高了预测精度。通过实例验证和结果对比,证明了该方法可显著提高月负荷预测的精度。
