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An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting

WATER RESOURCES MANAGEMENT 2021年第6期35卷 1757-1773页

作者： Huang, Haidong Zhang, Zhixiong Song, Fengxuan Beibu Gulf Univ Coll Civil Engn & Architecture Qinzhou 535011 Guangxi Peoples R China

Short-term water demand forecasting has always been a hot research topic in the field of water distribution systems, and many researchers have developed a wide variety of methods based on different prediction periodicities. However, few studies have paid attention to using ensemble learning methods for short-term water demand forecasting. In this study, an ensemble-learning-based method was developed to forecast short-term water demand. The proposed method consists of two models: an equal-dimension and new-information model and an ensemble learning model. The purpose of the equal-dimension and new-information model is to update data for modelling periodically, while the ensemble learning model is used for water demand forecasting. To evaluate the forecasting performance, the proposed method was applied to a data set obtained from a real-world water distribution system and compared with the single back-propagation neural network (BPNN) model and the seasonal autoregressive integrated moving average (SARIMA) model. The results show that the proposed method improves both the accuracy and stability of water demand prediction. The proposed method has the potential to provide a promising alternative for short-term water demand forecasting.

关键词： Ensemble learning Water demand forecasting Short-term adaptive boosting algorithm

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Data-driven prediction of the fatigue performance of corroded high-strength steel wires

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ENGINEERING FAILURE ANALYSIS 2023年第1期146卷

作者： Guo, Xuelian Liu, Xiaodong Long, Guanxu Zhao, Yue Yuan, Yangguang Changan Univ Highway Sch Xian 710064 Peoples R China Shandong Hispeed Construct Management Grp Co Ltd A1 Office Bldg Cent PlazaLongding Ave Jinan 250001 Shandong Peoples R China Shandong Hispeed Grp Co Ltd Innovat Res Inst Jinan 250001 Peoples R China Xian Univ Technol Sch Civil Engn & Architecture Xian 710048 Peoples R China Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Peoples R China

Corrosion damage determines the fatigue performance and intensifies the fatigue lifespan scatter of high-strength steel wires. Many experimental studies have been performed on corroded wires to quantify the correlation between fatigue life and corrosion. However, accurate analysis and fatigue performance prediction of various types of corrosion are still insufficient. This study proposes a predictive machine learning-based model to explore the quantitative influence of corrosion degree, stress ranges, loading ratio, loading frequency and other factors on the fatigue life of corroded high-strength steel wires. The proposed model is trained using the adaptive boosting algorithm on the basis of a collected database containing 323 samples from corrosion fatigue tests. A cross-validation method with the grid-search approach is applied to accelerate the optimisation of the hyperparameters. The performance of model is thoroughly compared with the empirical models and the common utilized machine learning algorithms. The prediction perfor-mance of the proposed model is considerably good, with a coefficient of determination of 0.96. The generalisation ability of the proposed model is superior to that of two common machine learning algorithms. Additionally, the physical meaning of the model is proven to be reasonable through a weight analysis of the input features. The stress range, corrosion rate and loading stress ratio dominate the fatigue performance of corroded high-strength steel wires.

关键词： Corroded high-strength wires Fatigue life Predictive model Machine learning adaptive boosting algorithm

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Application of hybrid artificial intelligence model to predict coal strength alteration during CO2 geological sequestration in coal seams

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年第0期711卷 135029-000页

作者： Yan, Hao Zhang, Jixiong Zhou, Nan Li, Meng China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Jiangsu Peoples R China Univ New South Wales Sch Minerals & Energy Resources Engn Sydney NSW 2052 Australia

CO2 geological sequestration in coal seams has gradually become one of the effective means to deal with the global greenhouse effect. However, the injection of CO2 into the coal seam can have an important impact on the physical and chemical properties of coal, which in turn affects the CO2 sequestration performance in coal seams and causes a large number of environmental problems. In order to better evaluate the strength alteration of coal in CO2 geological sequestration, a hybrid artificial intelligence model integrating back propagation neural network (BPNN), genetic algorithm (GA) and adaptive boosting algorithm (AdaBoost) is proposed. A total of 112 data samples for unconfined compressive strength (UCS) are retrieved from the reported studies to train and verify the proposed model. The input variables for the predictive model include coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure, and the corresponding output variable is the measured UCS. The predictive model performance is evaluated by correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The predictive results denote that the GA-BPNN-AdaBoost predictive model is an efficient and accurate method to predict coal strength alteration induced by CO2 adsorption. The simultaneous optimization of BPNN by GA and AdaBoost algorithm can greatly improve the prediction accuracy and generalization ability of the model. At the same time, the mean impact value (MIV) is used to investigate the relative importance of each input variable. The relative importance scores of coal rank, CO2 interaction time, CO2 interaction temperature and CO2 saturation pressure are 0.5475, 0.2822, 0.0373, 0.1330, respectively. The research results in this paper can provide important guiding significance for CO2 geological sequestration in coal seams. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Coal strength alteration Back propagation neural network Genetic algorithm adaptive boosting algorithm Predictive model

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Accident factor analysis based on different age groups via AdaBoost algorithm

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CANADIAN JOURNAL OF CIVIL ENGINEERING 2019年第5期46卷 364-370页

作者： Chen, Lihui Wang, Pin Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Univ Calif Berkeley PATH Richmond CA 94804 USA

A person's vision, perception, judgment, and operation of a vehicle decline with age. To analyze the influence of age on traffic accidents, we apply the adaptive boosting algorithm (AdaBoost) to investigate the most significant factors for two age groups (older and young driver groups) based on real-world accident data in California. Accident factors include gender, road type, pavement condition, weather, time of day, vehicle behavior, etc., as well as their corresponding subfactors. We first train some weak learners to find importance and then linearly combine those weak learners into a unified stronger learner. The proposed method has several advantages: (1) ability to handle unbalanced data, (2) no requirement on the assumption of data distribution, and (3) being robust for different datasets. Results show that the major factors regarding road safety for older drivers are weather and time of day, while for young drivers are traffic violations and vehicle behaviors.

关键词： traffic accident accident factor analysis adaptive boosting algorithm driver's age

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Synchronised measurements based transient security assessment of power systems using AdaBoost classifiers

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第17期13卷 3980-3987页

作者： Venkatesh, Thirugnanasambandam Jain, Trapti Indian Inst Technol Indore Discipline Elect Engn Indore 452020 Madhya Pradesh India VIT Bhopal Univ Sch Elect Engn Kothrikalan 466114 India

This study proposes an adaptive boosting (AdaBoost) algorithm for precise and accurate prediction of transient security assessment of power systems using synchronised measurements. The proposed approach (PRPA) also determines the generator coherent state as well as the synchronism status of each generating unit. The PRPA consists of three classifier models, in which classifier I determines the transient security status and classifier II is used to determine the generator coherency. Classifier III is a hybrid classifier, which determines the individual generator synchronism state for a given operating condition. This hybrid classifier consists of an array of parallel classifiers, where one classifier is assigned to each generating unit of the power system. For this assessment, the measured rotor angles of the generators are used as inputs to the proposed classifier models. In classification stage, several weak classifiers are combined in a linear manner to construct a strong classifier. The performance of the AdaBoost algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. Simulation results obtained from IEEE 14-bus, IEEE 30-bus and Indian 246-bus systems reveal that the PRPA can enhance the overall monitoring and assessment of power system using synchronised measurements.

关键词： synchronisation pattern classification fuzzy set theory power system security pattern clustering rotors learning (artificial intelligence) power engineering computing power system transients power system measurement AdaBoost classifiers precise prediction generator coherent state synchronism status generating unit classifier models transient security status classifier II generator coherency classifier III hybrid classifier parallel classifiers Indian 246-bus systems rotor angles synchronised measurementsbased transient security assessment generator synchronism state adaptive boosting algorithm power system transient security assessment weight updation strategy fuzzy clustering thresholding technique IEEE 30-bus systems IEEE 14-bus systems

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Towards an improved Adaboost algorithmic method for computational financial analysis

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2019年 134卷 219-232页

作者： Chang, Victor Li, Taiyu Zeng, Zhiyang Teesside Univ Sch Comp & Digital Technol Middlesbrough Cleveland England Xian Jiaotong Liverpool Univ Int Business Sch Suzhou Suzhou Peoples R China

Machine learning can process data intelligently, perform learning tasks and predict possible outputs in time series. This paper presents the use of our proposed machine learning algorithm;an adaptive boosting (Adaboost) algorithm, in analyzing and forecasting financial nonstationary data, and demonstrating its feasibility in financial trading. The data of future contracts are used in our analysis. The future used to test the Adaboost algorithm is a contract chosen to study future IF1711, which is combined by "HS300 index and Rb", the deformed steel bar future in Chinese stock market. The predicted data is compared with real world data to calculate accuracy and efficiency. The Adaboost algorithm is combined with an Average True Range-Relative Strength Index (ATR-RSI) strategy, so that it can be applied in automatic trading and therefore demonstrate its practical application We develop three additional algorithms to enable optimization, large sale simulations and comparing both the predicted and actual pricing values. We performed experiments and large scale simulations to justify our work. We have tested the accuracy and validity of our approach to improve its quality. In summary, our analysis and results show that our improved Adaboost algorithms may have useful and practical implications in nonstationary data analysis. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Nonstationary data Nonstationary time series Machine learning Back-propagation algorithm adaptive boosting algorithm ATR-RSI strategy

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An FPGA Implementation for Image Interpretation Based on adaptive boosting algorithm in the Real-Time Systems

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Procedia Technology 2012年 3卷 187-195页

作者： Mario-Alberto Ibarra-Manzano Dora-Luz Almanza-Ojeda Digital Signal Processing Laboratory Electronics Department DICIS University of Guanajuato Carretera Salamanca-Valle de Santiago Km.3.5+1.8 Km Comunidad Palo Blanco 36885 Salamanca Guanajuato Mexico Departamento de Ingenier«ıa Rob«otica Universidad Polit«ecnica de Guanajuato Av. Universidad Norte SN Comunidad Juan Alonso 38483 Cortazar Guanajuato Mexico

This paper presents an FPGA architecture for objects classiﬁcation based on adaptive boosting algorithm. The ar-chitecture uses the color and texture features as input attributes to discriminate the objects in a scene. Moreover, the architecture design takes into account the requirements of real-time processing. To this end, it was optimized for reusing the texture feature modules, giving, in this way, a more complete model for each object and becoming easier the object-discrimination process. The reuses technique allows to increase the information of the object model without decrease the performance or drastically increase the area used on the FPGA. The architecture classiﬁes 30 dense images per second of size 640 × 480 pixels. Both, classiﬁcation architec ture and optimization technique, are described and compared with others architectures founded in the literature. The conclusions and perspectives are given at the end of this document.

关键词： adaptive boosting algorithm Texture and Color Features FPGA Architecture Real-Time Systems

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AdaBoost classifiers for phasor measurements-based security assessment of power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第8期12卷 1747-1755页

作者： Thirugnanasambandam, Venkatesh Jain, Trapti Indian Inst Technol Indore Discipline Elect Engn Indore 452020 Madhya Pradesh India

Power system security is a major concern in real-time operation. It is essential to protect the system from blackout by taking proper control actions. This study proposes a boosting algorithm for the precise and accurate prediction of static security assessment of power systems using synchronised measurements. In addition to security status, the proposed approach also predicts the type of violations which may be either line overload/voltage violation or both of the insecure operating conditions. To overcome the computational complexity, the number of input phasor measurements is reduced by a statistical approach based on class separability and correlation coefficient indices. In the classification stage, support vector machines (SVMs) are used as weak classifiers and a strong classifier is constructed as the linear combination of many weak SVM classifiers. The performance of the adaptive boosting (AdaBoost) algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. The efficiency of the proposed approach is demonstrated on IEEE 14-bus, IEEE 30-bus, and Indian 246-bus systems. Further, the test results reveal that the proposed method of security assessment performs better than the other traditional classifiers viz. SVM, feed forward neural network and k-nearest neighbour classifier.

关键词： power system security phasor measurement learning (artificial intelligence) synchronisation computational complexity statistical analysis correlation methods support vector machines pattern clustering fuzzy set theory power engineering computing AdaBoost classifier power system security blackout static security assessment synchronised measurement line overload-voltage violation computational complexity input phasor measurement statistical approach correlation coefficient support vector machine weak SVM classifier adaptive boosting algorithm weight updation strategy fuzzy clustering thresholding technique IEEE 14-bus system IEEE 30-bus system Indian 246-bus system feed forward neural network k-nearest neighbour classifier

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Risk Factor Analysis of Traffic Accident for Different Age Group based on adaptive boosting 4

Risk Factor Analysis of Traffic Accident for Different Age G...

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4th International Conference on Transportation Information and Safety (ICTIS)

作者： Chen, Lihui Wang, Pin Tongji Univ Key Lab Rd & Traff Engn Minist Educ Coll Transportat Engn Shanghai Peoples R China

ISBN: (纸本)9781538604373

In order to explore the major traffic accident factors for different age groups, this paper adopts the accident data of Statewide Integrated Traffic Records System (SWITRS) for empirical study. 33,245 accident records in a state from January 1st, 2010 to December 31st, 2011 are selected. 8 accident factors, as well as their corresponding sub-factors, are analyzed for both senior-driver group and young-driver group. A data processing method for traffic accidents based on an adaptive boosting algorithm (AdaBoost) is proposed. According to the analysis results, the major factors that influence the driving safety of the senior driver group are weather and the time of day, the weight of which are 0.28 and 0.22, respectively. In particular, the rainy and snowy days, the sub-factors from the weather variable, influence old drivers most significantly. In contrast, the main factors that affect the driving safety of young people are traffic violation and vehicle behavior, with a weight of 0.25 and 0.19, respectively. Hence, it could be inferred that the diverse manifestation patterns of traffic accidents are related to drivers' age.

关键词： Traffic accident Risk facto analysis adaptive boosting algorithm Driver's age

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A New Method for the Prediction of the Gasoline Yield of the MIP Process

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PETROLEUM SCIENCE AND TECHNOLOGY 2015年第20期33卷 1713-1720页

作者： Jiang, H. Tang, J. Ouyang, F. E China Univ Sci & Technol Res Inst Petr Proc Shanghai 200237 Peoples R China

Themaximizing iso-paraffins (MIP) process, a family craft of fluid catalytic cracking process, is designed to reduce olefin content and increase isoparaffin and aromatic content in gasoline. The authors propose a method used to predict the gasoline yield of MIP process. The prediction model with 17 input variables is based on generalized regression neural network and adaptive boosting (adaboost) algorithm. The mean square error between predictive gasoline yield and actual yield is 2.46.

关键词： catalytic cracking gasoline yield generalized regression neural network adaptive boosting algorithm

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