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A Comparative Study of Oil-Water Two-Phase Flow Pattern Prediction Based on the GA-BP Neural Network and random forest algorithm

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PROCESSES 2023年第11期11卷 3155页

作者： Sun, Yongtuo Guo, Haimin Liang, Haoxun Li, Ao Zhang, Yiran Zhang, Doujuan 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 Sinopec Shengli Oilfield Co Res Inst Explorat & Dev Dongying 257000 Peoples R China

As global oil demand continues to increase, in recent years, countries have continued to expand the development of oil reserves, highlighting the importance of oil. In order to adapt to different strata distribution conditions, domestic drilling technology is becoming more and more perfect, resulting in a gradual increase in horizontal and inclined wells. Because of the influence of various downhole factors, the flow pattern in the wellbore will be more complex. Accurately identifying the flow pattern of multiphase flow under different well deviation conditions is very important to interpreting the production log output profile accurately. At the same time, in order to keep up with the footsteps of artificial intelligence, big data and artificial intelligence algorithms are applied to the oil industry. This paper uses the GA-BP neural network and random forest algorithm to conduct fluid flow pattern prediction research on the logging data of different water cuts at different inclinations and flow rates. It compares the predicted results with experimental fluid flow patterns. Finally, we can determine the feasibility of these two algorithms for predicting flow patterns. We use the multiphase flow simulation experiment device in the experiment. During the process, the flow patterns are observed and recorded by visual inspection, and the flow pattern is distinguished by referring to the theoretical diagram of the oil-water two-phase flow pattern. The prediction results show that the accuracy of these two algorithms can reach 81.25% and 93.75%, respectively, which verifies the effectiveness of these two algorithms in the prediction of oil-water two-phase flow patterns and provides a new idea for the prediction of oil-water two-phase flow patterns and other phases.

关键词： inclined well horizontal well vertical well GA-BP neural network random forest algorithm flow pattern prediction

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The TPRF: A Novel Soft Sensing Method of Alumina-Silica Ratio in Red Mud Based on TPE and random forest algorithm

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PROCESSES 2024年第4期12卷 663页

作者： Meng, Fanguang Shi, Zhiguo Song, Yongxing Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310013 Peoples R China Zhejiang JingLiFang Digital Technol Grp Co Ltd Hangzhou 310012 Peoples R China Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Compressor Technol Lab Anhui Prov State Key Lab Compressor Technol Hefei 230031 Peoples R China

The online measurement of the aluminum-silicon ratio of red mud in the dissolution stage of the Bayer alumina production process is difficult to achieve. The offline assay method has a high cost and strong time delay. Soft sensors are an effective and economical method to solve such problems. In this paper, a hybrid model (TPRF model) based on a tree-structured Parzen estimator (TPE) optimized random forest (RF) algorithm is proposed to measure the Al-Si ratio of red mud. The probability distribution of the hyperparameters of the random forest model is estimated by combining the TPE optimization algorithm with the random forest algorithm. According to this probability distribution, the hyperparameters of the random forest algorithm are adjusted in the parameter search space to obtain the best combination of hyperparameters. We established a TPRF soft sensing model based on the optimal combination of hyperparameters. The results show that the best performance of the TPRF model is a mean absolute percentage error (MAPE) of 0.0015, a root-mean-square error (RMSE) of 0.00378, a mean absolute error (MAE) of 0.00162, and a goodness of fit (R2) of 0.9893. The goodness of fit improved by 93.2% compared to the linear model, 39.1% compared to the SVR model, about 21.2% compared to the GRU model, and 5.5% compared to the RF model. This level of performance is demonstrated to be better than traditional soft sensors.

关键词： TPE algorithm random forest algorithm soft sensor alumina-silica ratio

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Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a random forest algorithm

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DRONES 2023年第7期7卷

作者： Ruseno, Neno Lin, Chung-Yan Guan, Wen-Lin Natl Formosa Univ Dept Power Mech Engn Huwei 632301 Taiwan Natl Formosa Univ Dept Aeronaut Engn Huwei 632301 Taiwan Taiwan Transportat Safety Board New Taipei City 231 Taiwan

In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems.

关键词： UAS traffic management conflict detection network remote ID inverted teardrop algorithm communication latency flight test random forest algorithm

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Analysis on the spatial dynamic characteristics of land use in the urban agglomeration in central Yunnan based on random forest algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2024年第1-2期27卷 92-109页

作者： Niu, Lede Lin, Jingzhi Zhou, Lifang Zhou, Yan Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China

In order to improve the accuracy of land use spatial analysis results, this paper takes the urban agglomeration in central Yunnan as an example, and proposes a land use spatial dynamic characteristics analysis method based on random forest algorithm. Using GIS technology to collect and process land remote sensing image data, we extract sparse description features of remote sensing images through the dictionary learning method, build a random forest classification model, classify land use space, and analyse dynamic features. The detailed analysis of land use change in the study area from 2005 to 2020 shows that the cultivated land area in this area has increased by 3,661 km(2), the dry land area has increased by 3,704 km(2), and the grassland area has decreased by 2,727 km(2), with the highest annual change rate of 0.62%.

关键词： remote sensing image land use spatial dynamic feature random forest algorithm sparse description feature

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Research on Diagnosis Method of Common Knee Diseases Based on Subjective Symptoms and random forest algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 156-168页

作者： Wang, Guangjun Hu, Mengxia Lv, Linlin Zhang, Hanyuan Sun, Yining Su, Benyue Ma, Zuchang Chinese Acad Sci Hefei Inst Phys Sci Inst Intelligent Machines Anhui Prov Key Lab Med Phys & Technol Hefei 230031 Peoples R China Univ Sci & Technol China Grad Sch Sci Isl Branch Hefei 230026 Peoples R China Anqing Normal Univ Univ Key Lab Intelligent Percept & Comp Anhui Pro Anqing 246013 Peoples R China Anhui Med Univ Affiliated Hosp 1 Dept Sports Med & Arthroscop Surg Hefei 230022 Peoples R China

Knee diseases are common diseases in the elderly, and timely and effective diagnosis of knee diseases is essential for disease treatment and rehabilitation training. In this study, we construct a diagnostic model of common knee diseases based on subjective symptoms and random forest algorithm to realize patients' self-initial diagnosis. In this paper, we first constructed a questionnaire of subjective symptoms of knee, and set up a questionnaire system to guide users to fill out the questionnaire correctly. Then clinical data collection is carried out to obtain clinical questionnaire data. Finally, the diagnostic analysis of three common diseases of knee joint is carried out by random forest machine learning method. Through leave-one-out cross validation, the accuracy of meniscus injury, anterior cruciate ligament injury and knee osteoarthritis diseases are 0.79, 0.84, 0.81 respectively;the sensitivity is 0.79, 0.84, 0.88 respectively;and the specificity is 0.80, 0.84, 0.79 respectively. The results show that the method can achieve a good effect of self- diagnosis, and can provide a knee joint disease screening a convenient and effective approach.

关键词： Knee diseases subjective symptoms random forest algorithm self-diagnosis

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Prediction of ground vibrations induced by bench blasting using the random forest algorithm

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JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY 2023年第3期123卷 123-132页

作者： Dzimunya, N. Besa, B. Nyirenda, R. Univ Zimbabwe Harare Zimbabwe Univ Zambia Lusaka Zambia

The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from bench blasting using 48 data records. The model was trained and tested using WEKA datamining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.

关键词： Blast-induced ground vibrations peak particle velocity random forest algorithm EPB equation

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OPTIMIZATION OF INTERNAL CONTROL FOR BUDGET OPERATIONS IN PUBLIC INSTITUTIONS BASED ON random forest algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1863-1871页

作者： Jin, Yang Henan Polytech Inst Sch Econ Nanyang 473000 Henan Peoples R China

In order to promote the construction of internal control in public institutions and improve work efficiency, the author proposes an optimization of internal control in public institution budget business based on random forest algorithm. We have constructed a big data audit framework for internal control of A Maritime Bureau based on the financial cloud platform and sorted out its audit process. By using the random forest algorithm to identify suspicious points in the internal control audit of administrative institutions at the data level, an example analysis is conducted using some data from A Maritime Bureau's assets, budget, revenue and expenditure, infrastructure, and contract business. The results indicate that the design of the internal control big data audit plan for administrative institutions will promote the innovation of audit information technology and application in A Maritime Bureau, provide theoretical guidance for the internal control big data audit carried out by administrative institutions, and effectively solve the problems of high workload and low work efficiency when A Maritime Bureau conducts internal control big data audits, thereby improving audit efficiency.

关键词： random forest algorithm Public utility units Budget business internal controls

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Utilizing random forest algorithm for early detection of academic underperformance in open learning environments

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1708页

作者： Balabied, Shikah Abdullah Albriki Eid, Hala F. Univ Bisha Coll Educ & Human Dev Dept Qual Life & Continuing Educ Bisha Saudi Arabia Univ Bisha Appl Coll Dept Business Adm Bisha Saudi Arabia

Background: One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disciplines, from computer science and engineering to humanities and social sciences. However, the scalability of OLEs also presents some challenges i.e., it can be too difficult to provide personalized support and feedback to individuals. Early prediction of student performance can improve the learning experience of students by providing early interventions and ***: The specific objective of this study was to build a model that identifies atrisk students and allows for timely interventions to promote their academic achievement. The random forest classifier model has been used for analyzing anonymized large datasets available from Open University Learning Analytics (OULAD) to identify patterns and relationships among various factors that contribute to student success or ***: The findings of this study suggest that this algorithm achieved 90% accuracy in identifying students who may be at risk and providing them with the necessary support to succeed.

关键词： Open learning environments random forest algorithm OULAD MOOCs Learning analytics

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Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

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NATURAL HAZARDS 2021年第1期107卷 331-353页

作者： Huang, Xiao-yan He, Li Zhao, Hua-sheng Huang, Ying Wu, Yu-shuang Guangxi Res Inst Meteorol Sci Nanning Peoples R China

The recent emergence of satellite detection and imaging technologies has increased the demand for the application of satellite cloud images to current weather forecasting. However, approaches based on nonlinear prediction technology to forecast satellite images are lacking, and forecasting timelines are relatively short, e.g., 1-3 h in advance. In the present study, a nonlinear dimensionality reduction approach based on Laplacian eigenmaps (LEs) was combined with a random forest (RF) algorithm to construct an intelligent computing prediction model for rainstorm satellite images obtained from the first annual rainy season (April-June) in South China from 2010 to 2018. Results showed that the proposed forecasting model based on nonlinear intelligent calculation can accurately predict the key features and trends of the development and movement of strong precipitation clouds. The predicted satellite images described by the model were also consistent with the major features of the observed satellite images. This study then used a multiple linear regression (MLR) method based on the same prediction factors to establish a model for predicting satellite images for the same modeling and forecasting samples. Comparative results of the two prediction schemes showed that the LE + RF algorithm satellite image prediction scheme yields more samples exhibiting a high correlation with observed satellite images than the MLR method. Compared with that of the proposed scheme, the amount of samples of the MLR scheme in the low-correlation area was significantly larger. In general, the nonlinear intelligent computing scheme developed in this study is superior to the MLR method for predicting satellite cloud images. Thus, the LE + RF algorithm satellite image prediction scheme provides an objective and practical method for observed satellite cloud image predictions.

关键词： Satellite cloud images Heavy rainfall Laplacian eigenmaps random forest algorithm Multiple linear regression

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Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm

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SCIENCE & JUSTICE 2021年第5期61卷 516-527页

作者： Hu, Lai Xing, Yu Jiang, Pu Gan, Li Zhao, Fan Peng, Wenli Li, Weihan Tong, Yanqiu Deng, Shixiong Chongqing Med Univ Dept Forens Med 1 Yixueyuan Rd Chongqing 400016 Peoples R China Chongqing Jiaotong Univ Sch Humanities 66 Xuefu Rd Chongqing 400016 Peoples R China

Gradual changes in microbial communities in a human body after death can be used to determine postmortem interval (PMI). In this study, gut microflora samples were collected from the vermiform appendix and the transverse colon of human cadavers with PMIs between 5 and 192 h. The results revealed that the appendix might be an excellent intestinal sampling site and the appendix flora had an inferred succession rule during human body decomposition. Firmicutes, Bacteroidetes, and their respective subclasses showed a predictable succession rule in relative abundance over time. A random forest regression model was developed to correlate human gut microbiota with PMI. We believe that our findings have increased the knowledge of the composition and abundance of the gut microbiota in human corpses, and suggest that the use of the human appendix microbial succession may be a potential method for forensic estimation of the time of death.

关键词： Human gut microbiome High-throughput sequencing Postmortem interval random forest algorithm

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