检索结果-南通市图书馆

FAIR machine learning model Pipeline Implementation of COVID-19 Data

Data Intelligence 2022年第4期4卷 971-990,1036页

作者： Sakinat Folorunso Ezekiel Ogundepo Mariam Basajja Joseph Awotunde Abdullahi Kawu Francisca Oladipo Abdullahi Ibrahim Department of Mathematical Sciences Olabisi Onabanjo UniversityP.M.B 2002Ago-IwoyeOgun StateNigeria 120005Nigeria Data Science Nigeria Lagos 105102Nigeria Leiden University 1011NCAmsterdamthe Netherlands Department of Computer Science University of IlorinIlorinKwara State240103Nigeria Department of Computer Science Ibrahim Badamosi UniversityLapaiNiger State911101Nigeria Kampala International University 260101Uganda Federal University Lokoja Nigeria Virus Outbreak Data Network-Africa

Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines(that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and(re)usability of data, so that new and robust analytical tools, such as machine learning(ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.

关键词： FAIRification Semantic data model Cluster analysis FAIR data Metadata machine learning model

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Economic Risk Assessment of Future Debris Flows by machine learning Method

引用

International Journal of Disaster Risk Science 2024年第1期15卷 149-164页

作者： Chenchen Qiu Lijun Su Alessandro Pasuto Giulia Bossi Xueyu Geng School of Engineering University of WarwickCoventry CV47ALUK Key Laboratory of Mountain Hazards and Earth Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Research Institute for Geo-Hydrological Protection(IRPI-CNR) National Research Council of ItalyPadova 35127Italy

A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.

关键词： Economic risk Future debris fows Gyirong Zangbo Basin machine learning model Physical vulnerability matrix Southwest Tibet,China

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

引用

Journal of Integrative Agriculture 2024年第4期23卷 1393-1408页

作者： Jie Song Dongsheng Yu Siwei Wang Yanhe Zhao Xin Wang Lixia Ma Jiangang Li State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Agricultural and Rural Bureau of Luanping County Luanping 068250China Chinese Academy of Sciences University Beijing 100049China

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(Pmodels.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

关键词： soil organic matter Sentinel-2 monthly synthetic images machine learning model spatial prediction

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Real-time prediction of tunnel face conditions using XGBoost Random Forest algorithm

引用

Frontiers of Structural and Civil Engineering 2023年第12期17卷 1777-1795页

作者： Lei-jie WU Xu LI Ji-dong YUAN Shuang-jing WANG Key Laboratory of Urban Underground Engineering of Ministry of Education Beijing Jiaotong UniversityBeijing 100044China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China

Real-time perception of rock conditions based on continuously collected data to meet the requirements of continuous Tunnel Boring machine(TBM)construction presents a critical challenge that warrants increased attention.To achieve this goal,this paper establishes real-time prediction models for fractured and weak rock mass by comparing 6 different algorithms using real-time data collected by the TBM.The models are optimized in terms of selecting metric,selecting input features,and processing imbalanced data.The results demonstrate the following points.(1)The Youden's index and area under the ROC curve(AUC)are the most appropriate performance metrics,and the XGBoost Random Forest(XGBRF)algorithm exhibits superior prediction and generalization performance.(2)The duration of the TBM loading phase is short,usually within a few minutes after the disc cutter contacts the tunnel face.A model based on the features during the loading phase has a miss rate of 21.8%,indicating that it can meet the early warning needs of TBM construction well.As the TBM continues to operate,the inclusion of features calculated from subsequent data collection can continuously correct the results of the real-time prediction model,ultimately reducing the miss rate to 16.1%.(3)Resampling the imbalanced data set can effectively improve the prediction by the model,while the XGBRF algorithm has certain advantages in dealing with the imbalanced data issue.When the model gives an alarm,the TBM operator and on-site engineer can be reminded and take some necessary measures for avoiding potential tunnel collapse.The real-time predication model can be a useful tool to increase the safety of TBM excavation.

关键词： Tunnel Boring machine fractured and weak rock mass machine learning model real-time early warming tunnel facerockcondition

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

引用

International Journal of Coal Science & Technology 2023年第2期10卷 83-96页

作者： Faming Huang Haowen Xiong Shixuan Chen Zhitao Lv Jinsong Huang Zhilu Chang Filippo Catani School of Civil Engineering and Architecture Nanchang UniversityNanchang 330031China Discipline of Civil Surveying and Environmental EngineeringPriority Research Centre for Geotechnical Science and EngineeringUniversity of NewcastleCallaghanNSW 2287Australia Department of Geosciences University of PadovaPaduaItaly

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

关键词： Slope stability prediction Long short-term memory Deep learning Geo-Studio software machine learning model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Evaluation of influential parameters for supersonic dehydration of natural gas: machine learning approach

引用

Petroleum Research 2022年第3期7卷 372-383页

作者： Emmanuel E.Okoro Uyiosa Igbinedion Victor Aimikhe Samuel E.Sanni Okorie E.Agwu Department of Petroleum and Gas Engineering University of Port HarcourtNigeria Department of Petroleum Engineering.Covenant University OtaNigeria Department of Chemical Engineering Covenant UniversityOtaNigeria Department of Petroleum Enginering University of UyoNigeria

The supersonic dehydration of natural gas is gaining more attention due to its numerous advantages over the conventional natural gas dehydration technologies.However,supersonic separators have seen minimal field applications despite the multiple benefits over other gas dehydration techniques.This has been mostly attributed to the uncertainty in ascertaining the design and operating parameters that should be monitored to ensure optimum dehydration of the supersonic separation device.In this study,the decision tree machine learning model is employed in investigating the effects of design and operating parameters(inlet and outlet pressures,nozzle length,throat diameter,and pressure loss ratio)on the supersonic separator performance during dehydration of natural gas.The model results show that the significant parameters influencing the shock wave location are the pressure loss ratio and nozzle length.The former was found to have the most significant effect on the dew point depression.The dehydration efficiency is mainly dependent on the pressure loss ratio,nozzle throat diameter,and the nozzle length.Comparing the machine learning model-accuracy with a 1-D iterative model,the machine learning model outperformed the 1-D iterative model with a lower mean average percentage error(MAPE)of 5.98 relative to 15.44 as obtained for the 1-D model.

关键词： Supersonic separator machine learning model Natural gas dehydration Separation efficiency

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Comparative Evaluation of machine learning models and Input Feature Space for Non-intrusive Load Monitoring

引用

Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1161-1171页

作者： Attique Ur Rehman Tek Tjing Lie Brice Valles Shafiqur Rahman Tito Department of Electrical and Electronic Engineering Auckland University of TechnologyAucklandNew Zealand Brice Valles Consulting AucklandNew Zealand School of Engineering and Trades Manukau Institute of TechnologyAucklandNew Zealand

Recent advancement in computational capabilities has accelerated the research and development of non-intrusive load disaggregation.Non-intrusive load monitoring(NILM)offers many promising applications in the context of energy efficiency and conservation.Load classification is a key component of NILM that relies on different artificial intelligence techniques,e.g.,machine learning.This study employs different machine learning models for load classification and presents a comprehensive performance evaluation of the employed models along with their comparative analysis.Moreover,this study also analyzes the role of input feature space dimensionality in the context of classification performance.For the above purposes,an event-based NILM methodology is presented and comprehensive digital simulation studies are carried out on a low sampling real-world electricity load acquired from four different households.Based on the presented analysis,it is concluded that the presented methodology yields promising results and the employed machine learning models generalize well for the invisible diverse testing data.The multi-layer perceptron learning model based on the neural network approach emerges as the most promising classifier.Furthermore,it is also noted that it significantly facilitates the classification performance by reducing the input feature space dimensionality.

关键词： machine learning model load feature non-intrusive load monitoring(NILM) comparative evaluation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

能源与人工智能(英文)

引用

能源与人工智能(英文) 2021年第1期3卷 180-191页

作者： Attique Ur Rehman Tek Tjing Lie Brice Valls Shafiqur Rahman Tito School of Engineering Computerand Mathematical SciencesAuckland University of TechnologyNew Zealand Brice Valles Consulting New Zealand School of Professional Engineering Manukau Institute of TechnologyNew Zealand

With today’s growth of prosumers and renewable energy resources,it is inevitable to incorporate the demand-side approaches for reliable and sustainable grid operation.In this context,demand response is a promising technique facilitating the consumers to play a substantial role in the energy market by altering their energy consumption patterns in times of peak demand or other critical contingencies.However,effective demand response deployment faces numerous challenges including trust deficit among the concerned stakeholders.This paper addresses the mentioned issue by proposing a non-invasive load-shed authentication model for demand response applications,assisted by an improved event-based non-intrusive load monitoring approach.For the said purposes,an improved event detection algorithm and machine learning model:support vector machine with a combination of genetic algorithm and GridSearchCV,is presented.This paper also presents a comprehensive real-world case study to validate the effectiveness of the proposed model in a real-life scenario.In the given context,all the simulations are carried out on low sampling real-world load measurements:Pecan Street-Dataport,where electric vehicle and air conditioning are employed as potential load elements for evaluation purposes.Based on the presented case study and analysis of the results,it is established that the presented improved event-based non-intrusive load monitoring approach yields promising performance in the context of multi-class classification.Moreover,it is also concluded that the proposed low sampling event-based non-intrusive load monitoring assisted non-invasive load-shed authentication model is a viable and promising solution for the effective implementation of demand response applications.

关键词： Non-Intrusive Load Monitoring Load-Shed Authentication Demand Response machine learning model Genetic Algorithm Energy Efficiency

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Pavement performance model for road maintenance and repair planning: a review of predictive techniques

Digital Transportation and Safety

引用

Digital Transportation and Safety 2023年第4期2卷 253-267页

作者： Krishna Singh Basnet Jagat Kumar Shrestha Rabindra Nath Shrestha Department of Civil Engineering Pulchowk CampusInstitute of EngineeringTribhuvan UniversityKathmanduP.O.Box 1915Nepal

This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users.

关键词： Road Maintenance Prediction model Deterministic model Probabilistic model machine learning model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Extreme gradient boosting algorithm based urban daily traffic index prediction model:a case study of Beijing,China

Digital Transportation and Safety

引用

Digital Transportation and Safety 2023年第3期2卷 220-228页

作者： Jiancheng Weng Kai Feng Yu Fu Jingjing Wang Lizeng Mao Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijing 100124China China Resources Wanxiang Life Shijiazhuang Wanxiang City Shijiazhuang 050031HebeiChina Beijing Municipal Transportation Operations Coordination Center Beijing 100073China

The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems.Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion.Compared with traditional short-time traffic prediction,this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index(TPI).The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation,including day of week,time period,public holiday,car usage restriction policy,special events,etc.Based on long-term historical TPI data,this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm(XGBoost).The model validation results show that the model prediction accuracy can reach higher than 90%.Compared with other prediction models,including Bayesian Ridge,Linear Regression,ElatsicNet,SVR,the XGBoost model has a better performance,and proves its superiority in large high-dimensional data sets.The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.

关键词： Traffic prediction Traffic performance index(TPI) Influencing factor XGBOOST machine learning model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：