Modeling of methane emissions using artificial neural network approach

Lidija, J; Aleksandra, A; Pocajt, Viktor

doi:10.2298/jsc020414110s

Cited by 14 publications

(19 citation statements)

References 28 publications

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“…Therefore, GRNN can avoid this problem of overfitting to a large extent. Meanwhile, the problem of initial values determination and local minima often occurs in training stage of BPNN, while this problem does not exist in the GRNN procedure [44]. Thus, the GRNN can be preferred over the BPNN.…”

Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

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Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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“…Large VIF values have been used as criteria for removing features from ANNs and other ML models. 51,52 Again, T, ρ, D 0, η, and all RDF features have high VIF values. This suggests that only a few of these features will be needed for the development of well-performing ANN models.…”

Section: ■ Results and Discussionmentioning

confidence: 96%

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

et al. 2021

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Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2) of ∼0.99 and a mean squared error of ∼2.9 × 10–5. Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

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“…In recent years, several intelligent algorithms have been proposed for the prediction of NH3 levels [9][10][11][12][13] .…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of Ammonia Levels In Geese Houses Based On Combined Feature Selector and Random Forest

Huang

Guo

Wu³

et al. 2022

Preprint

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Ammonia concentration (NH3) is a dominant source of environmental pollution in geese housing and profoundly affects the healthy growth of geese. Accurately forecasting NH3 and analyzing its change trends in geese houses is crucial for the survival of geese. To improve the prediction accuracy of NH3, we propose a novel forecasting model by the combination of feature selector (CFS) and random forest (RF). The developed model integrated two modules. First, combining mutual information (MI) and relief-F, we propose CFS quantify the importance values of each feature and eliminate the low-relation or unrelated features. Second, we built a random forest model and used the K-fold cross-validation grid search algorithm (CVGS) to obtain the RF hyper-parameters to predict NH3. The simulation results show that the prediction accuracy was improved when feature selection after quantification based on the CFS was used. The mean square error (MSE), root mean square error (RMSE) and mean absolute percent error (MAPE) for the proposed model were 0.5072, 0.6583, 2.88%, respectively. The NH3 prediction model based on CFS-GS-RF exhibited best prediction accuracy, and generalization performance compared with other parallel forecasting models and is a suitable and useful tool for predicting NH3 in geese houses.

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Modeling of methane emissions using artificial neural network approach

Cited by 14 publications

References 28 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

Short-Term Prediction of Ammonia Levels In Geese Houses Based On Combined Feature Selector and Random Forest

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