检索结果-内蒙古大学图书馆

Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

引用

ENERGY 2022年第PartA期239卷 121915-121915页

作者： Mulashani, Alvin K. Shen, Chuanbo Nkurlu, Baraka M. Mkono, Christopher N. Kawamala, Martin China Univ Geosci Key Lab Tecton & Petr Resources Minist Educ Wuhan 430074 Peoples R China China Univ Geosci Sch Earth Resources Dept Petr Geol Wuhan 430074 Peoples R China Mbeya Univ Sci & Technol Coll Engn & Technol Dept Geosci & Min Technol Box 131 Mbeya Tanzania

Permeability is the key variable for reservoir characterization used for estimating the flow patterns and volume of hydrocarbons. Modern computer advancement has highlighted the use of machine learning approaches such as group method of data handling (GMDH) in predicting permeability. However, the widely employed GMDH has intrinsic problems in its application. Therefore, the objective of this study is to present an enhanced GMDH based modified Levenberg-Marquardt (LM) as an improved alternative to conventional GMDH in predicting permeability from well logs. The study used natural gamma-ray, standard resolution formation density, limited effective porosity, shale volume of rock, and thermal neutron porosity well logs as input variables. Results show that an enhanced method has a reasonable reduction in processing time with high accuracy. Compared to conventional GMDH and backpropagation neural networks (BPNN), the GMDH-LM used 30% less computation time and performed excellently during training with the least error values of 0.092 and 0.018 for RMSE and MAE. Likewise, good results were observed during testing, obtaining the least error values of 0.679 and 0.056 for RMSE and MAE respectively. The modified generalization performance of GMDH-LM makes it an improved form of GMDH and can be adopted as an improved alternative in predicting permeability. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network permeability Well logs group method of data handling

来源：评论

学校读者我要写书评

暂无评论

Wavelet group method of data handling for fault prediction in electrical power insulators

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 123卷 106269-106269页

作者： Stefenon, Stefano Frizzo Dal Molin Ribeiro, Matheus Henrique Nied, Ademir Mariani, Viviana Cocco Coelho, Leandro dos Santos Menegat da Rocha, Diovana Fatima Grebogi, Rafael Bartnik de Barros Ruano, Antonio Eduardo Santa Catarina State Univ Elect Engn Grad Program Joinville Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil Fed Technol Univ Parana Dept Math Pato Branco Brazil Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Mech Engn Grad Program Curitiba Parana Brazil Univ Newcastle Sch Elect Engn & Comp Callaghan NSW Australia Fed Inst Santa Catarina Dept Ind Proc Lages SC Brazil Univ Algarve Fac Sci & Technol P-8005 Faro Portugal Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal

Electric power is increasingly being used in the globalized day-to-day and keeping the electric power system running is necessary. Insulators are important components of the electric power system. In case of failure in these components, there may be disconnections and, consequently, no electricity. Contaminated insulators can develop irreversible failures if they are not inspected. One equipment used for the inspection of the electric power system is the ultrasound, which generates an audible noise based on a time series that is used to identify possible failures. The time series forecast can be used for possible prediction of the development of failure. In this paper, a hybrid method that uses Wavelet Energy Coefficient (WEC) for feature extraction and group method of data handling (GMDH) for time series prediction is proposed, being defined as Wavelet GMDH. For comparison and validation of the proposed method, a benchmark is made with well-established algorithms such as Long Short-Term Memory (LSTM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). For a fairer analysis, these algorithms are also evaluated based on the same data extraction with WEC. The proposed method proved to have good accuracy comparing with LSTM and ANFIS, and is much faster than the compared methods.

关键词： group method of data handling Wavelet transform Electric power system

来源：评论

学校读者我要写书评

暂无评论

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training group method of data handling Using Extreme Learning Machine Conceptions

引用

WATER RESOURCES MANAGEMENT 2020年第2期34卷 529-561页

作者： Sabed-Movahed, Farid Najafzadeh, Mohammad Mehrpooya, Adel Grad Univ Adv Technol Fac Sci & Modern Technol Dept Appl Math Kerman Iran Grad Univ Adv Technol Fac Civil & Surveying Engn Dept Water Engn Kerman Iran Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran

Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of numerical models to estimate coefficient of longitudinal dispersion, these mathematical techniques may often come in quite few inaccuracies due to complex mechanism of convection-diffusion processes in pollutant transition in water pipelines. In this research work, to obtain more accurate prediction of LDC, general structure of group method of data handling (GMDH) is modified by means of extreme learning machine (ELM) conceptions. In fact, with getting inspiration from ELM, a novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage. In fact, an intermediate parameter is employed to establish a relationship between the input and output in each neuron of the GMDH model. In this way, a well-known and reliable dataset (233 experimental data) related to LDC in water network pipelines, as output vector, is applied to conduct training and testing phases. Through datasets, the Re number, the average longitudinal flow velocity, the friction factor of pipeline and the diameter of pipe are considered as inputs of the proposed approach. The results of GMDH-ELM model indicate a highly satisfying level of precision in both training and testing phases. Furthermore, feed forward structure of GMDH model was improved by particle swarm optimization (PSO) and gravitational search algorithm (GSA) to predict LDC. Through a sound judgment, a comparison is drawn between the performance of GMDH-ELM and other developed GMDH models. Moreover, several empirical equations existing in literature have been applied for compari

关键词： group method of data handling Extreme learning machine Evolutionary algorithms Longitudinal dispersion coefficient Water pipelines Least square problem

来源：评论

学校读者我要写书评

暂无评论

Prediction of subgrade reaction modulus of clayey soil using group method of data handling

引用

SCIENTIA IRANICA 2020年第4期27卷 1740-1750页

作者： Naeini, S. A. Moayed, R. Ziaie Kordnaeij, A. Mola-Abasi, H. Imam Khomeini Int Univ Dept Civil Engn Qazvin Iran Gonbadekavous Univ Dept Civil Engn Gonbadekavous Iran

Settlement-based design of foundations, using subgrade reaction modulus (K-s), is an important technique in geotechnical engineering. Plate Load Test (PLT) is one of the most commonly performed tests to directly obtain K-s of soil. As the determination of K-s by PLT -especially in greater depths- is both time-consuming and costly, it is necessary to develop models that can easily handle measurable characteristics. The suitability of the group method of data handling (GMDH) polynomial Neural Network (NN) to estimate K-s of clayey soils was investigated in the present research. In order to develop the GMDH models, 123 data sets from Qazvin, Iran, were applied. Predictability of the derived equations was compared with those of other available equations for clayey soil. The results demonstrated that an improvement in predicting K-s was achieved. Sensitivity analysis of the best GMDH-based equation showed that the Liquid Limit (LL) of soil was the most influential parameter on the proposed GMDH model in predicting K-s of clayey soil. (C) 2020 Sharif University of Technology. All rights reserved.

关键词： Subgrade reaction modulus Neural network Plate load test group method of data handling Clay

来源：评论

学校读者我要写书评

暂无评论

Scoring of Tenders in Construction Projects Using group method of data handling

引用

KSCE JOURNAL OF CIVIL ENGINEERING 2020年第7期24卷 1996-2008页

作者： Mehrabani, Masoud Nouri Golafshani, Emadaldin Mohammadi Ravanshadnia, Mehdi Islamic Azad Univ Sci & Res Branch Dept Civil Engn Tehran Iran

In a competitive construction environment, contractors are often faced with a large number of tenders that compel them to make the best decision in a limited time. In this paper, a decision support system (DSS) based on group method of data handling model (GMDH) was developed for the scoring of tenders (STs). Based on a comprehensive study of the existing literature and experts' opinions, seven criteria were extracted, namely client, contract, company, consulting engineers, project status/situation, risk, and economic criteria as the inputs of the system. To develop the DSS, the data from 135 tenders were collected from the previous tenders of a private construction contractor. The results illustrate that the proposed model, with a negligible error, is reliable in ST. Moreover, the proposed model specifies the score of new tenders, and decision-makers (DMs) can easily make their decisions in prioritizing and evaluating new tenders. Furthermore, a sensitivity analysis was performed to address the importance of the criteria, and it was concluded that the contract related criterion is the most important principle to the contractor. Eventually, a graphical user interface was designed in a user-friendly environment which allows the decision-makers to visualize the S T.

关键词： Scoring of tenders group method of data handling Construction projects Bidding Decision-making

来源：评论

学校读者我要写书评

暂无评论

Prediction of frictional characteristics of bituminous mixes using group method of data handling and multigene symbolic genetic programming

引用

ENGINEERING WITH COMPUTERS 2020年第4期36卷 1875-1888页

作者： Pattanaik, Madhu Lisha Choudhary, Rajan Kumar, Bimlesh Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 Assam India Indian Inst Technol Guwahati Dept Civil Engn Gauhati 781039 Assam India

A safe road transport requires adequate friction between vehicle tires and pavement surface for safe travels. Adequate friction is essential for a vehicle to safely maneuver. Inadequate friction is directly correlated to the accident hazard, particularly in wet weather conditions. Quality of pavement materials has a prodigious effect on skid resistance and high-quality materials allow adequate pavement frictional resistance for an extended period. Science evaluation of frictional characteristics depends on physical, chemical, and mineralogical properties of the aggregate, type of mix, binder/bitumen content, water film thickness, etc., and it necessities a costly and time-consuming test protocol. In the present research, a model is developed for the evaluation of skid resistance in terms of the British pendulum number (BPN), using experimental observations, with the aid of machine learning tools. In the present work, group method of data handling (GMDH) and multigene symbolic genetic programming (MSGP) have been used to model the BPN. Developed model is capable to simplify extremely nonlinear deviations in data as well as forecast the frictional performance from experimental data. It is also found that the performance of the MSGP (R-2 = 0.99) is more encouraging and better than that of the GMDH model (R-2 = 0.98) for the prediction of BPN. The analytical expression obtained through MSGP in the present study has been also subjected to sensitivity analysis to assess the effect of individual parameters in prediction of BPN.

关键词： Friction Skid resistance British pendulum number Multigene symbolic genetic programming group method of data handling

来源：评论

学校读者我要写书评

暂无评论

Application of cascade forward neural network and group method of data handling to modeling crude oil pyrolysis during thermal enhanced oil recovery

引用

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 205卷 108836-108836页

作者： Mohammadi, Mohammad-Reza Hemmati-Sarapardeh, Abdolhossein Schaffie, Mahin Husein, Maen M. Ranjbar, Mohammad Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Univ Calgary Dept Chem & Petr Engn Calgary AB Canada

Oil recovery during in situ combustion is majorly controlled by hydrocarbon oxidation and pyrolysis reactions, which govern fuel formation and heat evolution. Fuel deposition, in turn, can be accurately predicted in part through crude oil pyrolysis using thermogravimetry analysis (TGA). The theoretical models based on TGA runs, however, may be limited to crude oil samples at hand. In this study, we develop more general models to predict the residual mass during crude oil pyrolysis based on multi-layer perceptron (MLP), cascade forward (CFNN), generalized regression (GRNN), and radial basis function (RBF) neural networks. More than 2000 experimental data spanning wide range of weight percentages of asphaltenes and resins as well as oAPI gravities, heating rates, and temperatures are used. Moreover, six optimization algorithms;including Bayesian regularization (BR), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), conjugate gradient backpropagation with FletcherReeves updates (CGF), resilient backpropagation (RB), and conjugate gradient backpropagation with PolakRibie ' re updates (CGP) are used to improve the performance and prediction ability of the MLP and CFNN neural networks. The CFNN model optimized with the LM algorithm best fits all the experimental data with a mean absolute percent relative error of 1.04%. Lastly, a mathematical correlation is developed utilizing the group method of data handling (GMDH) to estimate the residual mass of crude oil pyrolysis in TGA. Despite its simplicity, the correlation also provides very good estimates. Sensitivity analysis showed that temperature followed by asphaltenes and resin content showed the highest effect on mass loss during crude oil pyrolysis. Outliers estimation applying the Leverage approach suggested only 1% of the data points could be doubtful.

关键词： In-situ combustion EOR Pyrolysis TGA Artificial neural networks group method of data handling

来源：评论

学校读者我要写书评

暂无评论

Effective thermal conductivity of sands estimated by group method of data handling (GMDH) 10

Effective thermal conductivity of sands estimated by Group M...

引用

10th International Conference of Materials Processing and Characterization (ICMPC)

作者： Rizvi, Zarghaam Haider Husain, Syed Mohammad Baqir Haider, Hasan Wuttke, Frank Univ Kiel Geomech & Geotech Kiel Germany AMU Dept Comp Engn Aligarh Uttar Pradesh India KIET Dept Informat Technol Ghaziabad India

Effective thermal conductivity (ETC), the ability of soil mass to dissipate heat is of vital scientific and engineering interest for many subsurface energy storage and transportation processes. Many mathematical, empirical, semi-empirical and numerical methods are available to compute the ETC. However, these methods offer a specific solution to a considered soil type but are limited by boundary conditions and the time of computation in case of numerical methods which are more general in nature and could be applied to a wider range of soil types. Neural networks due to superior prediction capabilities have attracted much attention in the recent past. Here, in this paper we apply group method of data handling (GMDH) from the class of neural network due to superior abilities in effectiveness of input parameter scaling and less computation time. Standard errors are calculated, and the model used to predict the ETC of two sands of different texture, mineralogical composition for full range of saturation. The model predicted experimental results are in good agreement for the pure quartz sand but for quartz-feldspar the model over predicts the ETC. The method is general and could be applied for wide range of geomaterial for effective parameter identification. © 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference of Materials Processing and Characterization.

关键词： Computation method Effective thermal conductivity group method of data handling Heat transport Neural network

来源：评论

学校读者我要写书评

暂无评论

Modeling heat capacity of ionic liquids using group method of data handling: A hybrid and structure-based approach

引用

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2019年 129卷 7-17页

作者： Rostami, Alireza Hemmati-Sarapardeh, Abdolhossein Karkevandi-Talkhooncheh, Abdorreza Husein, Maen M. Shahaboddin Shamshirband Rabczuk, Timon PUT Dept Petr Engn Ahvaz Iran Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Amirkabir Univ Technol Dept Petr Engn Tehran Iran Univ Calgary Dept Chem & Petr Engn Calgary AB T2N 1N4 Canada Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City Vietnam King Saud Univ Coll Comp & Informat Sci Dept Comp Engn Riyadh Saudi Arabia

Ionic liquids (ILs) are a significant class of chemicals with applications in solar cells, sensors, capacitors, batteries, plasticizers and thermal fluids. These compounds have attracted wide attention due to their low vapor pressure, tunable viscosity, non-flammability, wide liquid region phase diagrams and substantial chemical and thermal stability. Moreover, ILs structures can be easily modified leading to highly tunable physicochemical properties, which widen the application of these compounds. Heat capacity of ILs is an essential property for heat transfer evaluation as well as the estimation of widely used thermodynamic properties. Establishing a generalized and accurate model for predicting the heat capacity of ILs is valuable for their further development. In this manuscript, a hybrid group method of data handling (GMDH) was employed to establish a model estimating the ILs heat capacities. The database employed is an all-inclusive source of data taken from NIST standard, which includes the heat capacities of 56 ILs as a function of temperature and four structural parameters. About 80% of the database was assigned for building the model, and the remainder was used for evaluating the model performance. Statistical parameters and graphical techniques revealed that the model developed in this study is very accurate, with an R-2 value of 0.982 and an average absolute percent relative error (AAPRE) of 1.84%. Moreover, the sensitivity analysis showed that the chemical structure of the cation has the highest impact on the heat capacity of ILs. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Ionic liquids Heat capacity group method of data handling Model Comprehensive error analysis Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

A novel wind speed prediction method based on robust local mean decomposition, group method of data handling and conditional kernel density estimation

引用

ENERGY CONVERSION AND MANAGEMENT 2019年 200卷 112099-000页

作者： Jiang, Yan Liu, Shuoyu Peng, Liuliu Zhao, Ning Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400044 Peoples R China Sichuan Agr Univ Sch Civil Engn Chengdu 611830 Sichuan Peoples R China

Short-term wind speed prediction has a great practical significance in wind power exploitation and development. However, owing to strong nonstationarity and nonlinearity of natural wind, it is hard to obtain accurate and reliable prediction by traditional models. To this end, this paper develops an innovative hybrid model to reveal the different characteristics of wind speed data for obtaining the satisfactory forecasting capability. Besides the deterministic prediction, this method could also provide probabilistic prediction. Specifically, robust local mean decomposition is firstly implemented to address the nonstationarity and nonlinearity of wind speed time series by separating them into a series of different subseries with lower ones;Then, an unexplored group method of data handling neural network is developed to extract the nonlinear information in each subseries and subsequently perform the corresponding individual prediction;Furthermore, for the above modeling residuals, traditional conditional kernel density estimation with the embedded dimension determined by partial auto correlation function is employed to describe the other potential linear and nonlinear characteristics in the form of conditional probability, where the bandwidth matrices are optimized by normal reference criterion. On this basis, the deterministic prediction with the single-value is generated through aggregate calculation. In order to further consider the uncertainty in the data, the probabilistic prediction using unbiased conditional kernel density estimation is performed and the corresponding result is exhibited by prediction intervals. Case studies based on three groups of the measured wind speed data are conducted to comprehensively illustrate the effectiveness of the proposed method. The final results indicate that the proposed method occupies greater accuracy and higher reliability in comparison with the other concerned models.

关键词： Short-term wind speed prediction Robust local mean decomposition group method of data handling Traditional conditional kernel density estimation Unbiased conditional kernel density estimation Deterministic prediction Probabilistic prediction

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：