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    Tytuł:
    HydroProg: a system for hydraulic forecasting in real time, based on the multimodelling approach
    Autorzy:
    Niedzielski, T.
    Miziński, B.
    Kryza, M.
    Netzel, P.
    Wieczorek, M.
    Kasprzak, M.
    Migoń, P.
    Szymanowski, M.
    Jeziorska, J.
    Witek, M.
    Kosek, W
    Powiązania:
    https://bibliotekanauki.pl/articles/108558.pdf
    Data publikacji:
    2014
    Wydawca:
    Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
    Tematy:
    hydrology
    ensemble prediction
    multimodelling
    real time prognosis
    Kłodzko District
    Opis:
    Aleja Mickiewicza 24/28, 30-059 Kraków, Poland Abstract: The objective of this paper is to present the concept of a novel system, known as HydroProg, that aims to issue flood warnings in real time on the basis of numerous hydrological predictions computed using various models. The core infrastructure of the system is hosted by the University of Wrocław, Poland. A newly-established computational centre provides in real time, courtesy of the project Partners, various modelling groups, referred to as “project Participants”, with hydrometeorological data. The project Participants, having downloaded the most recent observations, are requested to run their hydrologic models on their machines and to provide the HydroProg system with the most up-to-date prediction of riverflow. The system gathers individual forecasts derived by the Participants and processes them in order to compute the ensemble prediction based on multiple models, following the approach known as multimodelling. The system is implemented in R and, in order to attain the above-mentioned functionality, is equipped with numerous scripts that manipulate PostgreSQL- and MySQL-managed databases and control the data quality as well as the data processing flow. As a result, the Participants are provided with multivariate hydrometeorological time series with sparse outliers and without missing values, and they may use these data to run their models. The first strategic project Partner is the County Office in Kłodzko, Poland, owner of the Local System for Flood Monitoring in Kłodzko County. The experimental implementation of the HydroProg system in the Nysa Kłodzka river basin has been completed, and six hydrologic models are run by scientists or research groups from the University of Wrocław, Poland, who act as Participants. Herein, we shows a single prediction exercise which serves as an example of the HydroProg performance.
    Źródło:
    Meteorology Hydrology and Water Management. Research and Operational Applications; 2014, 2, 2; 65-72
    2299-3835
    2353-5652
    Pojawia się w:
    Meteorology Hydrology and Water Management. Research and Operational Applications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Systematic Review of Ensemble Techniques for Software Defect and Change Prediction
    Autorzy:
    Khanna, Megha
    Powiązania:
    https://bibliotekanauki.pl/articles/2123249.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    ensemble learning
    software change prediction
    software defect prediction
    software quality
    systematic review
    Opis:
    Background: The use of ensemble techniques have steadily gained popularity in several software quality assurance activities. These aggregated classifiers have proven to be superior than their constituent base models. Though ensemble techniques have been widely used in key areas such as Software Defect Prediction (SDP) and Software Change Prediction (SCP), the current state-of-the-art concerning the use of these techniques needs scrutinization. Aim: The study aims to assess, evaluate and uncover possible research gaps with respect to the use of ensemble techniques in SDP and SCP. Method: This study conducts an extensive literature review of 77 primary studies on the basis of the category, application, rules of formulation, performance, and possible threats of the proposed/utilized ensemble techniques. Results: Ensemble techniques were primarily categorized on the basis of similarity, aggregation, relationship, diversity, and dependency of their base models. They were also found effective in several applications such as their use as a learning algorithm for developing SDP/SCP models and for addressing the class imbalance issue. Conclusion: The results of the review ascertain the need of more studies to propose, assess, validate, and compare various categories of ensemble techniques for diverse applications in SDP/SCP such as transfer learning and online learning.
    Źródło:
    e-Informatica Software Engineering Journal; 2022, 16, 1; art. no. 220105
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes
    Autorzy:
    Topór, Tomasz
    Sowiżdżał, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/27310145.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    machine learning
    model stacking
    ensemble method
    carbonates
    seismic attributes
    porosity prediction
    Opis:
    This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.
    Źródło:
    Geology, Geophysics and Environment; 2023, 49, 3; 245--260
    2299-8004
    2353-0790
    Pojawia się w:
    Geology, Geophysics and Environment
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Examining the Predictive Capability of Advanced Software Fault Prediction Models – An Experimental Investigation Using Combination Metrics
    Autorzy:
    Sharma, Pooja
    Sangal, Amrit Lal
    Powiązania:
    https://bibliotekanauki.pl/articles/2060915.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    process product
    process metrics
    classifiers
    ensemble design
    software
    fault prediction
    software quality
    Opis:
    Background: Fault prediction is a key problem in software engineering domain. In recent years, an increasing interest in exploiting machine learning techniques to make informed decisions to improve software quality based on available data has been observed. Aim: The study aims to build and examine the predictive capability of advanced fault prediction models based on product and process metrics by using machine learning classifiers and ensemble design. Method: Authors developed a methodological framework, consisting of three phases i.e., (i) metrics identification (ii) experimentation using base ML classifiers and ensemble design (iii) evaluating performance and cost sensitiveness. The study has been conducted on 32 projects from the PROMISE, BUG, and JIRA repositories. Result: The results shows that advanced fault prediction models built using ensemble methods show an overall median of $F$-score ranging between 76.50% and 87.34% and the ROC(AUC) between 77.09% and 84.05% with better predictive capability and cost sensitiveness. Also, non-parametric tests have been applied to test the statistical significance of the classifiers. Conclusion: The proposed advanced models have performed impressively well for inter project fault prediction for projects from PROMISE, BUG, and JIRA repositories.
    Źródło:
    e-Informatica Software Engineering Journal; 2022, 16, 1; art. no. 220104
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    IDENTYFIKACJA KOMPONENTÓW DESTRUKCYJNYCH W MODELACH PREDYKCYJNYCH W PODEJŚCIU WIELOMODELOWYM
    IDENTYFIKACJA KOMPONENTÓW DESTRUKCYJNYCH W MODELACH PREDYKCYJNYCH W PODEJŚCIU WIELOMODELOWYM IDENTIFICATION OF DESTRUCTIVE COMPONENTS IN PREDICTIVE MODELS WITH A MULTI-MODEL APPROACH
    Autorzy:
    Szupiluk, Ryszard
    Rubach, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/453339.pdf
    Data publikacji:
    2017
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
    Tematy:
    predykcja
    agregacja modeli
    ślepa separacja
    identyfikacja szumów
    prediction
    blind separation
    ensemble methods
    Theta noise measure
    Opis:
    W niniejszym artykule przedstawimy metodę identyfikacji komponentów destrukcyjnych występujących w podejściu wielomodelowym wykorzystującym algorytmy ślepej separacji sygnałów. Ocena charakterystyki poszczególnych komponentów dokonana zostanie na podstawie autorskich mierników zmienności/gładkości sygnałów. W celu potwierdzenia skuteczności prezentowanej metody przedstawimy praktyczny eksperyment poprawy wyników prognozy zużycia energii elektrycznej.
    In this paper we present a method of identification of destructive components in predictive models. This method may be applied in case of a multi-model approach and uses algorithms of blind signal separation. The evaluation of the characteristics of individual components will be based on the proposed metrics for evaluating the variation or smoothness of signals. In order to confirm the effectiveness of the presented method, we will present a practical experiment in which the results of the forecast of short-term electricity consumption are improved. Keywords: prediction, blind separation, ensemble methods, Theta noise measure
    Źródło:
    Metody Ilościowe w Badaniach Ekonomicznych; 2017, 18, 4; 679-688
    2082-792X
    Pojawia się w:
    Metody Ilościowe w Badaniach Ekonomicznych
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
    Autorzy:
    Trostianchyn, A.M.
    Izonin, I.V.
    Duriagina, Z.A.
    Tkachenko, R.O.
    Kulyk, V.V.
    Havrysh, B.M.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200577.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    Sm-Co alloy
    ensemble learning
    gradient boosting
    prediction accuracy
    Stop Sm-Co
    uczenie zespołowe
    dokładność przewidywania
    Opis:
    Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 116, 2; 71--80
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Local dynamic integration of ensemble in prediction of time series
    Autorzy:
    Osowski, S.
    Siwek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/201557.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neural networks
    ensemble of predictors
    dynamic integration
    time series prediction
    sieci neuronowe
    zespół predyktorów
    dynamiczna integracja
    Opis:
    The paper presents local dynamic approach to integration of an ensemble of predictors. The classical fusing of many predictor results takes into account all units and takes the weighted average of the results of all units forming the ensemble. This paper proposes different approach. The prediction of time series for the next day is done here by only one member of an ensemble, which was the best in the learning stage for the input vector, closest to the input data actually applied. Thanks to such arrangement we avoid the situation in which the worst unit reduces the accuracy of the whole ensemble. This way we obtain an increased level of statistical forecasting accuracy, since each task is performed by the best suited predictor. Moreover, such arrangement of integration allows for using units of very different quality without decreasing the quality of final prediction. The numerical experiments performed for forecasting the next input, the average PM10 pollution and forecasting the 24-element vector of hourly load of the power system have confirmed the superiority of the presented approach. All quality measures of forecast have been significantly improved.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 3; 517-525
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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