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Prediction of Oil-Water Two-Phase Flow Patterns Based on bayesian Optimisation of the XGBoost algorithm

PROCESSES 2024年第8期12卷 1660页

作者： Wang, Dudu Guo, Haimin Sun, Yongtuo Liang, Haoxun Li, Ao Guo, Yuqing Yangtze Univ Coll Geophys & Petr Resources Wuhan 430100 Peoples R China Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil-water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through bayesian techniques (using the bayesian-optimization library, version 1.4.3) to predict oil-water two-phase flow regimes. The integration of bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclinatio

关键词： horizontal well vertical well inclined well bayesian algorithm XGBoost algorithm flow pattern prediction

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Denoising Smooth Signals Using a bayesian Approach: Application to Altimetry

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2017年第4期10卷 1278-1289页

作者： Halimi, Abderrahim Buller, Gerald S. McLaughlin, Stephen Honeine, Paul Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland Univ Rouen Normandie Lab Informat Traitement Informat & Syst F-76000 Rouen France

This paper presents a novel bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The proposed bayesian model takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. In comparison with state-of-the-art algorithms, the proposed strategy provides a good compromise between denoising quality and necessary reduced computational cost. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation or a classification strategy.

关键词： Altimetry bayesian algorithm coordinate descent algorithm (CDA) gamma Markov random fields (gamma-MRFs)

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Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: bayesian Optimization and Feature Selection Approach

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REMOTE SENSING 2024年第21期16卷 3917页

作者： Zhang, Jun Cheng, Jinpeng Liu, Cuiping Wu, Qiang Xiong, Shuping Yang, Hao Chang, Shenglong Fu, Yuanyuan Yang, Mohan Zhang, Shiyu Yang, Guijun Ma, Xinming Henan Agr Univ Coll Agron Zhengzhou 450046 Peoples R China Minist Educ Key Lab Regulating & Controlling Crop Growth & Dev Zhengzhou 450046 Peoples R China Beijing Acad Agr & Forestry Sci Informat Technol Res Ctr Key Lab Quantitat Remote Sensing Agr Minist Agr & Beijing 100097 Peoples R China Henan Normal Univ Coll Software Xinxiang 453000 Peoples R China Henan Agr Univ Coll Informat & Management Sci Zhengzhou 450002 Peoples R China Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R2 values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

关键词： leaf area index hyperparameter optimization bayesian algorithm random forests

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Generalized prediction and optimal operating parameters of PCDD/F emissions by explainable bayesian support vector regression

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WASTE MANAGEMENT 2021年 135卷 437-447页

作者： Xiong, Shijian Peng, Yaqi Lu, Shengyong Shang, Fanjie Li, Xiaodong Yan, Jianhua Cen, Kefa Zhejiang Univ State Key Lab Clean Energy Utilizat Hangzhou 310027 Peoples R China Zhejiang Fuchunjiang Environm Technol Res Co Ltd Hangzhou 311401 Peoples R China

The current derived models for predicting polychlorinated dibenzo-p-dioxins and -furans (PCDD/F) emissions from incineration can only be applied to a specific incinerator due to high deviation or systematic errors. And the models fail to provide quantized guidance for the operation of full-scale municipal solid waste incinerators. To address the problem, explainable bayesian support vector regression (E-BSVR) has been established to generalized predict and maximumly reduce the PCDD/F emissions. First, forty-two PCDD/F samples were determined from a whole year experiment in a full-scale incinerator. Meanwhile, 1,2,4-trichlorobenzene(1,2,4-TrCBz), carbon monoxide, sulfur dioxide, oxynitride, particulate matter, fluoride, and hydrogen chloride were measured, as input features. Second, after box-cox transformation normalization, and hyperparameters tuning, the R-Squared and root mean square error (RMSE) of the proposed method are 0.983 and 0.044, exhibiting high accuracy. The high accuracy (R-Squared = 0.992) and generalization are also proven on the dataset with high PCDD/F emissions. Then, the performances of BSVR are compared with kernel ridge regression, multiple linear regression, and unary linear regression, indicating afar smaller RMSE of BSVR. Finally, the optimal operating parameters are calculated through local interpretable model-agnostic explanations and the partial dependence plot. Results indicate that reducing the content of organic chlorine in municipal solid waste and inhibiting the deacon reaction are important methods for reducing PCDD/F emissions. The optimal operating parameters for the maximal reduction of PCDD/F emissions are 1,2,4-TrCBz < 0.098 ug/m(3), fluoride > 0.452 mg/m(3). As a whole, the E-BSVR method can be used as a reliable and accurate approach for the prediction and reduction of PCDD/F emissions.

关键词： PCDD/F Support vector regression bayesian algorithm Model interpretation Optimal operating parameters

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bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2016年第4期54卷 2207-2219页

作者： Halimi, Abderrahim Mailhes, Corinne Tourneret, Jean-Yves Snoussi, Hichem Univ Technol Troyes Inst Charles Delaunay CNRS F-10010 Troyes France Univ Toulouse IRIT INPENSEEIHT TeSA F-31071 Toulouse France

This paper proposes a new bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non -identically Gaussian distribution. We introduce a prior enforcing a smooth temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The proposed bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason -2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms.

关键词： Altimetry bayesian algorithm coordinate descent algorithm delay/Doppler altimetry (DDA) natural gradient simultaneous autoregression (SAR) altimetry smooth estimation

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Predicting and analyzing the cementing quality of oil well reservoirs based on bayesian-random forest model

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 241卷

作者： Shi, Yue Sun, Jinfei Li, Zaoyuan Yang, Fujie Yang, Xin Luo, Qianmei Southwest Petr Univ Natl Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu Peoples R China Southwest Petr Univ Petr Engn Sch Chengdu Peoples R China Southwest Petr Univ Sch Sci Chengdu Peoples R China Southwest Petr Univ Inst Artificial Intelligence Chengdu Peoples R China

Cementing quality crucially influences the safety, efficiency, and costs of oil and gas wells. Traditional evaluations rely on empirical and qualitative methods, introducing uncertainty. This study analyzed nearly 2000 wells casing and cementing data from 2016 to 2021 in an eastern China oil field. The original data was analyzed using multiple data cleaning techniques, and effectively dealing with missing values and anomalies. Additionally, the application of feature selection techniques such as random forest successfully identified key elements within the data. Three machine learning models and hyperparameter optimization methods were compared. A bayesian random forest (BS-RF) model was established with hyperparameter optimization for cementing-quality evaluation. Finally, the relationship between the influencing factors and well-cementing quality were determined by the SHapley Additive exPlanations (SHAP) method. This enabled the evaluation and prediction of well cementing quality. The results indicate that the model achieved an accuracy of 93.21%. When the validation set was used for verification, the predictive accuracy was 95%. This indicates that the model is relatively stable and exhibits strong generalization capabilities across diverse datasets. Performance numeric of cement slurry (SPN), drilling fluid density before cementing, deviation angle, and flushing time are the four key features that influence cementing quality. Each has a SHAP value above 0.12. This article provides guidance for future well-cemented construction schemes.

关键词： Cementing Correlation coefficient bayesian algorithm Random forest model SHAP

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bayesian-based analysis of sequence activity characteristics in the Bohai Rim region

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APPLIED GEOPHYSICS 2024年 1-15页

作者： Bi, Jin-Meng Song, Cheng Cao, Fu-Yang Tianjin Earthquake Agcy Tianjin 300201 Peoples R China China Earthquake Adm Inst Geophys Beijing 100081 Peoples R China

Disaster mitigation necessitates scientific and accurate aftershock forecasting during the critical 2 h after an earthquake. However, this action faces immense challenges due to the lack of early postearthquake data and the unreliability of forecasts. To obtain foundational data for sequence parameters of the land-sea adjacent zone and establish a reliable and operational aftershock forecasting framework, we combined the initial sequence parameters extracted from envelope functions and incorporated small-earthquake information into our model to construct a bayesian algorithm for the early postearthquake stage. We performed parameter fitting and early postearthquake aftershock occurrence rate forecasting and effectiveness evaluation for 36 earthquake sequences with M >= 4.0 in the Bohai Rim region since 2010. According to the results, during the early stage after the mainshock, earthquake sequence parameters exhibited relatively drastic fluctuations with significant errors. The integration of prior information can mitigate the intensity of these changes and reduce errors. The initial and stable sequence parameters generally display advantageous distribution characteristics, with each parameter's distribution being relatively concentrated and showing good symmetry and remarkable consistency. The sequence parameter p-values were relatively small, which indicates the comparatively slow attenuation of significant earthquake events in the Bohai Rim region. A certain positive correlation was observed between earthquake sequence parameters b and p. However, sequence parameters are unrelated to the mainshock magnitude, which implies that their statistical characteristics and trends are universal. The bayesian algorithm revealed a good forecasting capability for aftershocks in the early postearthquake period (2 h) in the Bohai Rim region, with an overall forecasting efficacy rate of 76.39%. The proportion of "too low" failures exceeded that of "too high" failures, and the num

关键词： earthquake sequences bayesian algorithm model parameters correlation analysis effectiveness evaluation

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Integrative bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes

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CELL REPORTS 2018年第1期24卷 238-251页

作者： Xu, Lin Zheng, Yanbin Liu, Jing Rakheja, Dinesh Singleterry, Sydney Laetsch, Theodore W. Shern, Jack F. Khan, Javed Triche, Timothy J. Hawkins, Douglas S. Amatruda, James F. Skapek, Stephen X. Univ Texas Southwestern Med Ctr Dallas Dept Pediat Div Hematol Oncol Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Dept Clin Sci Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Dept Pathol Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Dept Mol Biol Dallas TX 75390 USA Univ Texas Southwestern Med Ctr Dallas Harold C Simmons Comprehens Canc Ctr Dallas TX 75390 USA Childrens Med Ctr Childrens Hlth Gill Ctr Canc & Blood Disorders Dallas TX 75235 USA NCI Pediatr Oncol Branch Ctr Canc Res Bethesda MD 20892 USA NCI Oncogen Sect Genet Branch Ctr Canc Res Bethesda MD 20892 USA USC Keck Sch Med Dept Pathol Los Angeles CA USA USC Keck Sch Med Dept Pediat Los Angeles CA USA Seattle Childrens Hosp Div Pediat Hematol Oncol Seattle WA USA Univ Washington Fred Hutchinson Canc Res Ctr Seattle WA 98195 USA

Identifying oncogenic drivers and tumor suppressors remains a challenge in many forms of cancer, including rhabdomyosarcoma. Anticipating gene expression alterations resulting from DNA copy-number variants to be particularly important, we developed a computational and experimental strategy incorporating a bayesian algorithm and CRISPR/Cas9 "mini-pool'' screen that enables both genome-scale assessment of disease genes and functional validation. The algorithm, called iExCN, identified 29 rhabdomyosarcoma drivers and suppressors enriched for cell-cycle and nucleic-acid-binding activities. Functional studies showed that many iExCN genes represent rhabdomyosarcoma line-specific or shared vulnerabilities. Complementary experiments addressed modes of action and demonstrated coordinated repression of multiple iExCN genes during skeletal muscle differentiation. Analysis of two separate cohorts revealed that the number of iExCN genes harboring copy-number alterations correlates with survival. Our findings highlight rhabdomyosarcoma as a cancer in which multiple drivers influence disease biology and demonstrate a generalizable capacity for iExCN to unmask disease genes in cancer.

关键词： rhabdomyosarcoma bayesian algorithm CRISPR/Cas9 oncogene tumor suppressor gene integrative genomic analysis childhood cancer

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Potential miRNA-disease association prediction based on kernelized bayesian matrix factorization

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GENOMICS 2020年第1期112卷 809-819页

作者： Chen, Xing Li, Shao-Xin Yin, Jun Wang, Chun-Chun China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

Many biological experimental studies have confirmed that microRNAs (miRNAs) play a significant role in human complex diseases. Exploring miRNA-disease associations could be conducive to understanding disease pathogenesis at the molecular level and developing disease diagnostic biomarkers. However, since conducing traditional experiments is a costly and time-consuming way, plenty of computational models have been proposed to predict miRNA-disease associations. In this study, we presented a neoteric bayesian model (KBMFMDA) that combines kernel-based nonlinear dimensionally reduction, matrix factorization and binary classification. The main idea of KBMFMDA is to project miRNAs and diseases into a unified subspace and estimate the association network in that subspace. KBMFMDA obtained the AUCs of 0.9132, 0.8708, 0.9008 +/- 0.0044 in global and local leave-one-out and five-fold cross validation. Moreover, KBMFMDA was applied to three important human cancers in three different kinds of case studies and most of the top 50 potential disease-related miRNAs were confirmed by many experimental reports.

关键词： microRNA Disease Association prediction bayesian algorithm Conjugate probabilistic model Matrix factorization

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Web page classification using modified naive bayesian approach

Web page classification using modified naive Bayesian approa...

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IEEE Region 10 Conference (TENCON 2006)

作者： Tomar, G. S. Verma, Shekhar Jha, Ashish Jaypee Inst Engn & Technol Guna 473226 India ABV Indian Inst Informat Technol & Management Gwalior India

ISBN: (纸本)9781424405480

This paper introduces the concept of a classification tool for web pages called WebClassify, which uses modified naive bayesian algorithm with multinomial model to classify pages into various categories. The tool starts the classification from downloading training web text from internet, preparing the hypertext for mining, and then storing web data in a local database. The paper also gives an account of choosing naive bayesian approach over other approaches for web text mining. The experimental results along with the classification accuracy analysis with increasing vocabulary size, is also shown.

关键词： bayesian algorithm competency ontology stemming

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