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Quantification of the concrete freeze–thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms

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Journal of Mountain Science 2024年第1期21卷 322-334页

作者： QIN Yanhui MA Haoyuan ZHANG Lele YIN Jinshuai ZHENG Xionghui LI Shuo Department of Civil Engineering Qingdao University of TechnologyQingdao 266520China Department of Geography Qinghai Normal UniversityXining 810008China

The reasonable quantification of the concrete freezing environment on the Qinghai–Tibet Plateau(QTP) is the primary issue in frost resistant concrete design, which is one of the challenges that the QTP engineering managers should take into account. In this paper, we propose a more realistic method to calculate the number of concrete freeze–thaw cycles(NFTCs) on the QTP. The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7. Four machine learning methods, i.e., the random forest(RF) model, generalized boosting method(GBM), generalized linear model(GLM), and generalized additive model(GAM), are used to fit the NFTCs. The root mean square error(RMSE) values of the RF, GBM, GLM, and GAM are 32.3, 4.3, 247.9, and 161.3, respectively. The R^(2) values of the RF, GBM, GLM, and GAM are 0.93, 0.99, 0.48, and 0.66, respectively. The GBM method performs the best compared to the other three methods, which was shown by the results of RMSE and R^(2) values. The quantitative results from the GBM method indicate that the lowest, medium, and highest NFTC values are distributed in the northern, central, and southern parts of the QTP, respectively. The annual NFTCs in the QTP region are mainly concentrated at 160 and above, and the average NFTCs is 200 across the QTP. Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP.

关键词： Freeze–thaw cycles Quantification machine learning algorithms Qinghai–Tibet Plateau Concrete

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Medium- and Long-Term Precipitation Forecasting Method Based on Data Augmentation and machine learning algorithms

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 1000-1011页

作者： Tang, Tiantian Jiao, Donglai Chen, Tao Gui, Guan Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210023 Peoples R China Nanjing Hydraul Res Inst Hydrol & Water Resources Dept Nanjing 210029 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China

Accurate medium and long-term precipitation forecasting plays a vital role in disaster prevention and mitigation and rational allocation of water resources. In recent years, there are various methods for medium- and long-term precipitation forecasting based on machine learning algorithms. However, machine learning has a high demand for the size of sample data. Therefore, this article proposes a data augmentation algorithm based on the K-means clustering algorithm and synthetic minority oversampling technique (SMOTE), which can effectively enhance sample information. Besides, through constructing random forest (RF), extreme gradient boosting (XGB), recurrent neural network (RNN), and long short-term memory (LSTM) are, respectively, constructed as the models to forecast monthly grid precipitation of the Danjiangkou River Basin. This study aims to improve the accuracy of medium- and long-term precipitation forecasting. The main results are the following two aspects: 1) in most years, the anomaly correlation coefficient and Pg score of SMOTE-km-XGB and SMOTE-km-RF exceed that of XGB and RF;furthermore, compared with the other three methods, SMOTE-km-XGB method is more suitable for precipitation forecasting in the studied basin in this article;and 2) the forecasting results of two deep learning methods (RNN and LSTM) show that the sample data processed by the K-means clustering algorithm and SMOTE data augmentation algorithm have not achieved considerable results in deep learning. This study improves the accuracy of precipitation forecast by expanding and balancing the information of sample data, and provides a new research idea for improving the accuracy of medium- and long-term hydrological forecasting.

关键词： Forecasting Clustering algorithms machine learning Data models Predictive models machine learning algorithms Computational modeling Extreme gradient boosting (XGB) K-means long short-term memory (LSTM) machine learning (ML) medium- and long-term precipitation forecasting random forest (RF) recurrent neural network (RNN) synthetic minority oversampling

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Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

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JOURNAL OF BIG DATA 2021年第1期8卷 1-21页

作者： Ataei, Atousa Majidi, Niloufar Seyed Zahiri, Javad Rostami, Mehrdad Arab, S. Shahriar Rizvanov, Albert A. Kazan Fed Univ Inst Fundamental Med & Biol Kazan Russia Tarbiat Modares Univ Fac Biol Sci Dept Biophys Tehran Iran Tarbiat Modares Univ Fac Biol Sci Dept Biophys Bioinformat & Computat Omics Lab BioCOOL Tehran Iran Univ Kurdistan Dept Comp Engn Sanandaj Iran

Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naive Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

关键词： machine learning algorithms Network topology Cancer Drug resistance Classification Feature selection

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Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1607-1622页

作者： Sami, Balahaha Hadi Ziyad Khai, Wong Jee Sami, Balahaha Fadi Ziyad Fai, Chow Ming Essam, Yusuf Ahmed, Ali Najah El-Shafie, Ahmed Univ Tenaga Nas Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Civil Engn Inst Sustainable Energy ISE Kajang 43000 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Civil Engn Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

This research studies the implementation of artificial neural networks (ANN) in predicting the concentration of total suspended solids (TSS) for the Fei Tsui reservoir in Taiwan. The prediction model developed in this study is designed to be used for monitoring the water quality in the Fei Tsui reservoir. High concentrations of total suspended solids (TSS) have been a crucial problem in the Fei Tsui reservoir for decades. As the Fei Tsui reservoir is a primary water source for Taipei City, this issue impacts the drinking water supply for the city due to etherification problems in the reservoir. 10-year average monthly records and 13-year average annual records have been collected for 26 parameters and correlated with the TSS concentrations to determine the parameters that have a strong relationship with the TSS concentrations. The parameters that were shown to have a strong correlation with the TSS concentration are the trophic state index (TSI), nitrate (NO3) concentration, total phosphorous (TP) concentration, iron concentration (IRON), and turbidity. Linear regression was used to develop the model that estimates the TSS concentration in the Fei Tsui Reservoir. The results show that model 3, a three-layer ANN model that uses three-input parameters namely NO3 concentration, TP concentration, and turbidity, with five neurons, to predict the output parameter which is TSS concentration, produces the highest coefficient of determination (R-2) and Willmott Index (WI), which are 0.9589 and 0.9933 respectively, and the lowest root mean square error, which is 0.4753. Based on these performance criteria, model 3 is concluded as the best model to predict TSS concentrations in this study. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Total suspended solids Water quality machine learning algorithms Time-series prediction and feitsui reservoir

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Influence of elevation on plastic-covered greenhouse mapping accuracy using high-resolution satellite imagery and machine learning algorithms

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REMOTE SENSING LETTERS 2023年第4期14卷 413-422页

作者： Veettil, Bijeesh Kozhikkodan Acharki, Siham Van Lang Univ Sci & Technol Adv Inst Lab Ecol & Environm Management Ho Chi Minh City Vietnam Van Lang Univ Fac Appl Technol Sch Technol Ho Chi Minh City Vietnam Abdelmalek Essaadi Univ Fac Sci & Tech Tangier Dept Earth Sci Tetouan Morocco

Proliferation of plastic-covered greenhouse (PCG) farming areas has been unparalleled in recent decades around the globe. Therefore, PCG's proper mapping and continuous monitoring are required to maintain protected farming sustainability and to reduce plastic pollution. Topography is one of the key factors affecting PCG mapping accuracy from satellite imagery. In this research, in order to understand the influence of elevation on PCG mapping accuracy in a Moroccan (with altitude below 200 m) and Vietnamese (an average altitude of 1,500 m) area, five supervised algorithms were applied to high-resolution PlanetScope and RapidEye imagery: maximum likelihood classification (MLC), k-nearest neighbours (k-NN), random forest (RF), support vector machine with linear kernel (SVML) and radial basis kernel (SVMRB). The findings indicated that k-NN (SVMRB) produced the greatest overall accuracy, followed by SVMRB (RF) for Dalat City (Loukkos). The present study also found that RapidEye outperforms PlanetScope in both areas and independently of the machine learning algorithm. Elevation data inclusion significantly improves (1.6% to 6.8%) various satellites' performance, particularly in Dalat City. Thus, this study demonstrated that topography has an impact on PCG mapping, especially in hilly areas.

关键词： High-resolution imagery topography plastic-covered greenhouse protected horticulture machine learning algorithms

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Response prediction of laced steel-concrete composite beams using machine learning algorithms

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STRUCTURAL ENGINEERING AND MECHANICS 2018年第3期66卷 399-409页

作者： Thirumalaiselvi, A. Verma, Mohit Anandavalli, N. Rajasankar, J. CSIR Struct Engn Res Ctr Acad Sci & Innovat Res CSIR Campus Madras 600113 Tamil Nadu India

This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite(1) (LSCC) beams proposed by Anandavalli et al (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen min an experiment are used to validate nonlinear FE model of the LSCC beams The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz, Support Vector Regression (SVR), Minimax Probability machine Regression (MPMR), Relevance Vector machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.

关键词： composite structures machine learning algorithms ultimate strength displacement

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Attendance automatic recognition and learning behavior of web-based course attendance based on machine learning algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1769-1777页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou 213032 Jiangsu Peoples R China

At present, the automatic attendance mode of distance education is not conducive to the confirmation and analysis of information after class. In order to study the effective automatic recognition algorithm of remote education classroom, this study takes the educational classroom of intelligent innovation and entrepreneurship of Internet + as an example for analysis. Moreover, this paper adopts facial features as the basis of recognition, establishes corresponding positioning points, and constructs precise positioning methods for real-time feature capture. At the same time, the ASM algorithm is used to extract facial features, and the algorithm is improved to improve the extraction effect. In addition, this paper proposes Gabor-wavelet packet set and Gabor beamlet set for auxiliary recognition, which improves the recognition rate. Finally, this paper designs experiments to analyze the performance of the algorithm of this study. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for subsequent related research.

关键词： Feature recognition automatic identification personnel management machine learning algorithms

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Detection and Classification of Conflict Flows in SDN Using machine learning algorithms

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IEEE ACCESS 2021年 9卷 76024-76037页

作者： Khairi, Mutaz Hamed Hussien Ariffin, Sharifah Hafizah Syed Latiff, Nurul Mu'Azzah Abdul Yusof, Kamaludin Mohamad Hassan, Mohamed Khalafalla Al-Dhief, Fahad Taha Hamdan, Mosab Khan, Suleman Hamzah, Muzaffar Univ Teknol Malaysia UTM Sch Elect Engn Fac Engn Johor Baharu 81310 Malaysia Future Univ Fac Engn Khartoum 10553 Sudan Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Univ Malaysia Sabah Fac Comp & Informat Kota Kinabalu 88400 Sabah Malaysia

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows.

关键词： machine learning algorithms Classification algorithms Support vector machines Control systems Decision trees Software algorithms Computer networks Software-defined network conflict flows detection flow classification machine learning algorithms

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Utilizing machine learning algorithms for prediction of the rheological behavior of ZnO (50%)-MWCNTs (50%)/ Ethylene glycol (20%)-water (80%) nano-refrigerant

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 156卷

作者： Song, Xiedong Baghoolizadeh, Mohammadreza Alizadeh, As'ad Basem, Ali Jasim, Dheyaa J. Sultan, Abbas J. Salahshour, Soheil Piromradian, Mostafa JiNing Univ Sch Comp Sci & Engn JiNing 273155 Peoples R China Inner Mongolia Univ Finance & Econ Sch Comp Informat Management Hohhot 010000 Peoples R China Shahrekord Univ Dept Mech Engn Shahrekord *** Iran Cihan Univ Erbil Coll Engn Dept Civil Engn Erbil Iraq Al Amarah Univ Coll Dept Petr Engn Maysan Iraq Warith Al Anbiyaa Univ Fac Engn Karbala 56001 Iraq Univ Technol Iraq Dept Chem Engn Baghdad Iraq Missouri Univ Sci & Technol Dept Chem & Biochem Engn Rolla MO 65409 USA Istanbul Okan Univ Fac Engn & Nat Sci Istanbul Turkiye Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkiye Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Khomeinishahr Iran

This paper aims to explore the utilization of machine learning techniques for the accurate prediction of rheological properties in a specific nanofluid system, ZnO(50 %)-MWCNTs (50 %)/Ethylene glycol (20 %)-water (80 %), designed for nano-refrigeration applications. The effective manipulation of the rheological behavior of nanofluids is pivotal for enhancing their heat transfer efficiency and overall performance. By harnessing the predictive power of machine learning, this study endeavors to unravel the intricate relationships governing the rheological characteristics of the nano-refrigerant, ultimately contributing to the development of advanced cooling solutions. The obtained results show that pnf of ZnO(50%)-MWCNTs (50%)/ Ethylene glycol(20%)-water (80%) nano-refrigerant is little affected by T, and even when T varies, this result does not alter much. Also, the lowest pnf occurs when it has the highest temperature and the lowest gamma and m. Finally, it was concluded that the best algorithm in terms of the Taylor diagram for pnf output is the MPR algorithm and the worst is the ECR algorithm and the pattern of gamma changes shows that the ideal value of gamma is the biggest when pnf levels fall in tandem with their growth.

关键词： machine learning algorithms Rheological behavior Nano -refrigerant

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Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data (vol 42, pg 2928, 2015)

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第19期42卷 6806-6806页

作者： Park, Byeonghwa Bae, Jae Kwon Hannam Univ Dept Business Stat Taejon 306791 South Korea Keimyung Univ Dept Management Informat Syst Daegu 704701 South Korea

House sales are determined based on the Standard & Poor’s Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naïve Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction.

关键词： Housing price index Housing price prediction model machine learning algorithms C4.5 RIPPER Naï ve Bayesian AdaBoost

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