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stacking ensemble algorithm to Predict Re-keying in Group Key Management

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-15页

作者： Kumari, Prity Singh, Karam Ratan Kumar, Ranjan Natl Inst Technol Arunachal Pradesh Dept Basic & Appl Sci Jote 791113 India Natl Inst Technol Arunachal Pradesh Dept Mech Engn Jote 791113 India

Group key management offers a flexible and reliable security mechanism for secure communication in wireless sensor network by assisting with suitable adjustments of the number of keys per node and the number of re-keying messages. In this article, we obtained a datasets using a projective plane after removing a single element. We employ a stacking ensemble algorithm to predict the re-keying value in a projective plane. To improve the performance of the prediction in the stacking model, adaptive boosting and random forest models are chosen as base learners, and for the meta-learner, linear regression is chosen. We observed that the stacking ensemble algorithm demonstrated higher accuracy compared to individual models. The accuracy of the stacking ensemble algorithm is found to be 0.9999, with MAE, MSE, and RMSE values of 0.0026, 0.0000, and 0.0030 respectively.

关键词： Adaptive boosting algorithm Group key management Linear regression Projective plane Random forest Re-keying stacking ensemble algorithm Wireless sensor network

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Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm

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Underground Space 2023年第6期13卷 241-261页

作者： Hu Luo Yong Fang Jianfeng Wang Yubo Wang Hang Liao Tao Yu Zhigang Yao Key Laboratory of Transportation Tunnel Engineering of Ministry of Education Southwest Jiaotong UniversityChengdu 610031China

Rockburst is a kind of common geological disaster in deep tunnel *** has the characteristics of causing great harm and occurring at random locations and *** characteristics seriously affect tunnel construction and threaten the physical and mental health and safety of ***,it is of great significance to study the tendency of rockburst in the early stage of tunnel survey,design and *** present,there is no unified method and selected parameters for rockburst *** view of the large difference of different rockburst criteria and the imbalance of rockburst database categories,this paper presents a two-step rockburst prediction method based on multiple factors and the stacking ensemble *** the influence of rock physical and mechanical parameters,tunnel face conditions and excavation disturbance,multiple rockburst criteria are predicted by integrating multiple machine learning algorithms.A combined prediction model of rockburst criteria is established,and the results of each rockburst criterion index are weighted and combined,with the weight updated using the field rockburst *** dynamic weight is combined with the cloud model to comprehensively evaluate the regional rockburst *** results from applying the model in the Grand Canyon tunnel show that the rockburst prediction method proposed in this paper has better applicability and higher accuracy than the single rockburst criterion.

关键词： Rockburst stacking ensemble algorithm Combined prediction Cloud model

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Intelligent prediction method for fracture pressure based on stacking ensemble algorithm

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GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES 2023年第1期9卷 1-24页

作者： Zhang, Hao Ren, Yangfeng Zhang, Yan Zheng, Shuangjin Yangtze Univ Sch Petr Engn Wuhan 430100 Peoples R China China Univ Petr Natl Key Lab Petr Resources & Engn Beijing 102249 Peoples R China Key Lab Drilling & Prod Engn Oil & Gas Wuhan 430100 Hubei Peoples R China

Fracture pressure is an important reference for wellbore stability analysis and hydraulic fracturing. Considering the low prediction accuracy, significant deviations, and limited applicability of traditional methods for predicting formation fracture pressure, this paper proposes an intelligent prediction method for fracture pressure using conventional well logging data based on the stacking ensemble algorithm. The base learners of the model include RF, KNN, and LSTM algorithms with low correlation. The meta-learner adopts the XGBoost algorithm. The effectiveness of the model is validated using the fracture pressure data from Dagang Oilfield. The prediction results indicate that the stacking algorithm outperforms individual algorithms. After optimization with genetic algorithm, the R2 of the stacking model is 0.989, RMSE is 0.009%, and MAE is 0.32%. The global sensitivity analysis results show that AC and DEN in the well logging data have higher sensitivity to the fracture pressure. When using intelligent fracture pressure prediction methods, it is essential to ensure the accuracy of AC and DEN data. The work demonstrates the reliability and effectiveness of the method proposed for the intelligent prediction of fracturing pressure using conventional well logging data through stacking ensemble algorithm to overcome the limitations of traditional methods. An intelligent prediction method of fracture pressure based on conventional logging data was *** prediction model is based on stacking ensemble algorithm, which outperforms individual algorithms in terms of *** sensitivity analysis shows that AC and DEN in logging data exhibit high sensitivity to fracture pressure.

关键词： Intelligent prediction stacking ensemble algorithm Fracture pressure Logging data Sensitivity analysis

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Estimation of seismic wave incident angle using vibration response data and stacking ensemble algorithm

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COMPUTERS AND GEOTECHNICS 2021年 137卷 104255-104255页

作者： Zhang, Jiawen Li, Mingchao Han, Shuai Deng, Genhua Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China

The determination of the incident angle of an earthquake is one of the critical research issues in modeling the seismic input mechanism at a dam site and it is also for geological exploration. At present, the incident angle calculation methods mostly rely on accurate geological models, complex theoretical formulations, and complicated procedures. To this end, an incident angle estimation method using dam vibration response data and a machine learning algorithm is presented in this study. First, a three-dimensional finite element gravity damfoundation system model is constructed. A wave input method based on the viscous-spring artificial boundary is used to simulate the multi-angle incidence of P and SV waves. The vibration responses of key dam points are obtained. Nine features that have geometric interpretation are constructed by the response data and the new data are substituted into a stacking ensemble algorithm for training. Finally, the angles of obliquely incident P and SV waves are estimated using the trained stacking ensemble estimation model. The results reveal correlation between dam responses and the incident angle with the average R2 values of the estimation model of 0.996 (P waves) and 0.996 (SV waves), and the average root mean square errors of 1.765? (P waves) and 0.546? (SV waves). It is thus confirmed that the estimation model integrated multiple features from several measurement points with high accuracy and stability. In addition, the proposed method can be extended to other types of large structures because of its universality.

关键词： Incident angle of an earthquake Dam-foundation system Vibration response data stacking ensemble algorithm Wave input method

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A stacking ensemble machine learning model for improving monthly runoff prediction

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-27页

作者： Wang, Wen-chuan Gu, Miao Li, Zong Hong, Yang-hao Zang, Hong-fei Xu, Dong-mei North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China

Accurate runoff prediction is significant for many tasks, such as water resource allocation, flood control, disaster reduction, and water conservancy project operation and scheduling in river basins. However, it is difficult to accurately predict using a single model due to the characteristics of runoff series, such as non-stationary, strong randomness, and complexity. We propose a new runoff prediction model based on the stacking (CNN-BiLSTM) ensemble learning algorithm. This model integrates the echo state network (ESN) model, stacked autoencoder (SAE) model, support vector machines (SVM) model, and long short-term memory (LSTM) model as the base-learner and the CNN-BiLSTM hybrid model as the meta-learner, further improving prediction accuracy through cross-validation. First of all, considering the difference in data observation and training principles of different prediction models, the Spearman correlation coefficient (SCC) method is used to analyze the correlation between the prediction errors of multiple single models and the mean absolute percentage error (MAPE) is used to evaluate their prediction ability. The model with low correlation and strong prediction ability is selected as the first layer prediction model of the stacking ensemble algorithm, that is, the base-learner. Then, the prediction results of the base-learner are used as a new input feature set and passed on to the second layer prediction model of the stacking ensemble learning model, namely the meta-learner, for training and prediction to obtain the final monthly runoff prediction value. To verify the prediction accuracy and generalization ability of the stacking (CNN-BiLSTM) ensemble model, Manwan hydropower station in southwest China, Xiajiang hydrological station in eastern China, and Wangjiahui hydrological station in northern China are selected as research objects. The model's performance is assessed using four evaluation metrics and compared against eight different models, and the predic

关键词： Monthly runoff prediction stacking ensemble algorithm k-fold cross-validation Spearman correlation analysis CNN-BiLSTM hybrid neural networks

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WNPS-LSTM-Informer: A Hybrid stacking model for medium-term photovoltaic power forecasting with ranked feature selection

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RENEWABLE ENERGY 2025年 244卷

作者： Li, Yifan Liu, Gang Cao, Yisheng Chen, Jiawei Xiao, Gang Tang, Jianchao Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China Shanghai Xilong Solar Energy Technol Co Ltd Shanghai 201517 Peoples R China 2588 Changyang Rd Shanghai Peoples R China

With the increasing adoption of photovoltaic (PV) technology in smart grids, accurate photovoltaic power forecasting (PVPF) is critical to grid stability. However, due to the stochastic nature of solar energy and the neglect of cyclical and trending patterns of PV power generation, existing methods struggle in midterm forecasting. To address these challenges, this paper proposes a hybrid model, WNPS-LSTM-Informer, for medium-term PVPF based on stacking ensemble algorithm. First, this paper optimizes the neural prophet (NP) by introducing the genetic and mutation operations of the genetic algorithm (GA) in the white shark optimizer (WSO). The NP is initially predicted and combined with meteorological data, and the relevant features are filtered to improve robustness using the ranked feature selection (RFS) algorithm proposed in this paper. The data are then fed into the stacking model, which integrates the long short-term memory (LSTM) and the Informer model to handle medium-term data more efficiently than a single model. In this paper, the model is validated using historical data from the photovoltaic system in Uluru, Australia, and compared with five stateof-the-art forecasting models at different time scales and seasons. The results show that WNPS-LSTM-Informer outperforms existing methods in medium-term forecasting.

关键词： Medium-term photovoltaic power prediction stacking ensemble algorithm Long short-term memory Informer

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stacking algorithm based framework with strong generalization performance for ultra-short-term photovoltaic power forecasting

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ENERGY 2025年 322卷

作者： Cao, Yisheng Yong, Pei Yu, Juan Yang, Zhifang Chongqing Univ State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China

Standard forecasting methods are commonly applicable to specific types of photovoltaic (PV) systems, and the prediction accuracy drops significantly for PV systems in different modules or locations. This paper proposes an ultra-short-term PV power forecasting framework based on the stacking ensemble algorithm (StAB), which achieves high precision prediction and good generalization performance through multi-model integration and data mining. Firstly, StAB utilizes Spearman's rank correlation coefficient to select appropriate features to reduce computational complexity. In addition, a correlation guided fast Fourier transform (CGFFT) is proposed to choose reasonably the optimal decomposition frequency, and decompose PV power into high-frequency and low-frequency components to reduce the prediction difficulty. Importantly, StAB builds an ensemble prediction model based on the stacking algorithm, by solving optimization problems in the base models and meta-model selection process, enabling the ensemble model to maintain desired performance for different distributed PV systems. The framework is tested on 12 PV power datasets, and the results demonstrate that the proposed framework outperforms 12 advanced artificial intelligence models and another two stacking algorithm based models, in terms of prediction accuracy and generalization performance.

关键词： Distributed photovoltaic power forecasting Generalization performance stacking ensemble algorithm Data mining

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Short-term power load forecasting based on SKDR hybrid model

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ELECTRICAL ENGINEERING 2024年 1-17页

作者： Yuan, Yongliang Yang, Qingkang Ren, Jianji Mu, Xiaokai Wang, Zhenxi Shen, Qianlong Li, Yanan Henan Polytech Univ Sch Mech & Power Engn Jiaozuo 454003 Peoples R China Henan Polytech Univ Sch Software Jiaozuo 454003 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China Jilin Univ Sch Commun Engn Changchun 130012 Peoples R China

Load forecasting is an important index to ensure the stable operation of power system. In recent years, load forecasting methods based on machine learning algorithms have received extensive attention. However, for such a complex problem, the traditional load forecasting method based on machine learning cannot solve the problem efficiently and accurately. Therefore, the ensemble learning method has gradually entered the field of view of researchers. Among them, stacking methods based on heterogeneous learners have received less attention. To that end, support vector machine (SVR), K-nearest neighbor (KNN) and decision tree (DT) are used as the base learners, and random forest (RF) is used as the meta-learner to construct a novel stacking ensemble learning model (SKDR) in this study. Besides, due to hyperparameters are essential elements affecting the predicted result, the sparrow search optimization algorithm is introduced to obtain the optimal combination of hyperparameters. The effectiveness and advancement of SKDR is validated on a real-world dataset. Experimental results showed that compared with traditional methods, the proposed method could provide competitive prediction results, that is R2 = 0.984/0.987, RMSE = 1.315/1.253, MAPE = 0.146/0.163, this illustrates the SKDR's potential in terms of load forecasting. The performance of SKDR is also verified on the open-source dataset.

关键词： Load forecasting stacking ensemble algorithm Sliding time window Sparrow search algorithm

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Wind turbine generator early fault diagnosis using LSTM-based stacked denoising autoencoder network and stacking algorithm

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2024年第11期21卷 2477-2492页

作者： Yan, Junshuai Liu, Yongqian Meng, Hang Li, Li Ren, Xiaoying North China Elect Power Univ Sch New Energy Beijing Peoples R China North China Elect Power Univ Sch New Energy Beijing 102206 Peoples R China

To reduce the significant economic losses caused by the fault deterioration of wind turbine generators, it is urgent to detect and diagnose the early faults of generators. The existing condition monitoring and fault diagnosis (CMFD) methods have disadvantages of less considering data temporal characteristic, acquiring early faults with difficulty, and having lower diagnostic accuracy. To address those limitations, a novel LSDAE-stacking CMFD method of generators was proposed. Specifically, a multivariate spatiotemporal condition monitoring model (LSDAE) was established by combining the LSTM and SDAE networks, which can detect generator early anomalies through real-time monitoring the reconstruction residual. Then, based on the stacking ensemble algorithm, a multi-classification fault diagnosis model (stacking) was constructed to identify early fault types, which can integrate advantages of different base-classifiers to achieve a better diagnostic accuracy. Case studies on three actual generator failures were employed to validate the effectiveness and accuracy of the proposed LSDAE-stacking method. The results illustrated that, compared with conventional SDAE model, the proposed LSDAE model had higher reconstruction precision and superior early-fault-warning capacities. And compared with traditional algorithms such as SVM, RF, AdaBoost, GBDT and XGBoost, the constructed stacking model can effectively identify the fault types of generators and had higher diagnostic accuracy.

关键词： Wind turbine generator condition monitoring fault diagnosis stacked denoising autoencoder network long short-term memory network stacking ensemble algorithm

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CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm

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RADIOLOGIA MEDICA 2020年第1期125卷 87-97页

作者： Mostafaei, Shayan Abdollahi, Hamid Dehkordi, Shiva Kazempour Shiri, Isaac Razzaghdoust, Abolfazl Moghaddam, Seyed Hamid Zoljalali Saadipoor, Afshin Koosha, Fereshteh Cheraghi, Susan Mahdavi, Seied Rabi Kermanshah Univ Med Sci Fac Med Dept Community Med Sorkheh Ligeh Blvd Kermanshah *** Iran Univ Tehran Med Sci Rheumatol Res Ctr Epidemiol & Biostat Unit Tehran Iran Kerman Univ Med Sci Dept Radiol Sci & Med Phys Fac Allied Med Med Univ CampusHaft Bagh Highway Kerman *** Iran Univ Texas Hlth Sci Ctr San Antonio Dept Cell Syst & Anat Sch Med San Antonio TX 78229 USA Geneva Univ Hosp Dept Med Imaging Div Nucl Med & Mol Imaging CH-1211 Geneva Switzerland Shahid Beheshti Univ Med Sci Urol & Nephrol Res Ctr Student Res Comm Tehran Iran Iran Univ Med Sci Sch Med Dept Med Phys Tehran Iran Shahid Beheshti Univ Med Sci Shohada E Tajrish Hosp Dept Radiat Oncol Fac Med Tehran Iran Shahid Beheshti Univ Med Sci Allied Med Fac Radiol Technol Dept Tehran Iran Iran Univ Med Sci Allied Med Fac Dept Radiat Sci Tehran Iran Iran Univ Med Sci Radiat Biol Res Ctr Tehran Iran

Purpose Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters. Methods In this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic. Results Sixty-four prostate cancer patients were studied, and 33 and 52 patients developed >= grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively. Conclusions We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.

关键词： Tomography Prostate cancer Radiotherapy Toxicity stacking ensemble algorithm

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