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    Wyświetlanie 1-2 z 2
    Tytuł:
    Stacking Artificial Intelligence Models for Predicting Water Quality Parameters in Rivers
    Autorzy:
    Almadani, Mohammad
    Kheimi, Marwan
    Powiązania:
    https://bibliotekanauki.pl/articles/2202356.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    dissolved oxygen
    water quality
    ensemble-stacking model
    meta-learner
    Opis:
    Scrutinizing the changes in the quality of river water is one of the main factors of monitoring the quality of natural flows, which plays a crucial role in the sustainable management of these ecosystems. The concentration of dissolved oxygen (DO) in river water is one of the most important indicators of quality management in such water bodies. From an environmental point of view, exceeding the permissible and natural decay capacity of pollutants in natural streams leads to a decrease in DO and, consequently, causes serious risks for the survival of aquatic life in related ecosystems. Hence, in the present study, 10 daily variables with the amount of dissolved oxygen on the same day were collected and evaluated from Allen County. Moreover, half of these variables were chosen as effective inputs to the model based on statistical analysis, so as to calculate the dissolved oxygen concentration parameter. Modeling with artificial intelligence approaches was implemented in the form of four individual methods: ANFIS-PSO, OS-ELM, Bagging-RF and Boosting CART, and two ensemble-stacking methods: SMA and Meta-learner MLP. The outcomes of estimating the DO with RMSE, MAE, GRI, r, and MBE criteria and marginal-scatter and subject profile diagrams were discussed. Moreover, the efficiency of the models in estimating the outlier of the observational data was scrutinized by subject profile diagram. Finally, it was found that the Meta-learner MLP model with RMSE of 0.965 mg/L had improvement in performance by 8.8%, 8.9%, 22.3%, 24.9% and 27.6%, respectively, compared to SMA, Boosting CART, Bagging-RF, ANFIS-PSO and OS-ELM methods. This remarkable improvement led to recommendations for using stacking techniques in water quality modeling and simulation.
    Źródło:
    Journal of Ecological Engineering; 2023, 24, 2; 152--164
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    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
      Wyświetlanie 1-2 z 2

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