Design of modeling error PDF based fuzzy neural network for effluent ammonia nitrogen prediction

Qiao, Junfei; Quan, Limin; Yang, Cuili

doi:10.1016/j.asoc.2020.106239

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…In recent years, several intelligent algorithms have been proposed to predict NH 3 levels [9][10][11][12][13] . Artificial neural networks (ANN), support vector machine (SVR), and decision tree (DT) are useful tools.…”

Section: Introductionmentioning

confidence: 99%

Short-term prediction of ammonia levels in goose houses via combined feature selector and random forest

Huang,

Gul Hassan,

et al. 2023

International Journal of Agricultural and Biological Engineering

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Ammonia concentration (NH 3 ) is a dominant source of environmental pollution in geese housing and profoundly affects the healthy growth of geese. Accurately forecasting NH 3 and analyzing its change trends in geese houses is crucial for the survival of geese. A novel forecasting model was proposed by combining feature selector (CFS) and random forest (RF) to improve the prediction accuracy of NH 3 in this study. The developed model integrated two modules. First, combining mutual information (MI) and relief-F, we propose that CFS quantify each feature's importance values and eliminate the low-relation or unrelated features. Second, a random forest model was built using K-fold cross-validation grid search algorithm (CVGS) to obtain the RF hyperparameters to predict NH 3 . 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, and 2.88%, respectively. The NH 3 prediction model (CFS-CVGS-RF) based on Combined Feature Selector, cross-validation grid search algorithm (CVGS), and Random Forest (RF) exhibited the best prediction accuracy and generalization performance compared with other parallel forecasting models and is a suitable and useful tool for predicting NH 3 in geese houses. The results of the research can provide a reference for the machine learning method to monitor the dynamic changes of ammonia in goose houses.

show abstract

Section: Introductionmentioning

confidence: 99%

Short-term prediction of ammonia levels in goose houses via combined feature selector and random forest

Huang,

Gul Hassan,

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

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

Section: Introductionmentioning

confidence: 99%

“…Denormalize the output to obtain the results in the normal dimension. The denormalize process is described by: (11) where 𝑦 𝑖 is the denormalized data, 𝑦 𝑚𝑎𝑥 and 𝑦 𝑚𝑖𝑛 are max and min value of original data, and 𝑦 𝑖 ′ and 𝑦 are the original data and its mean.…”

mentioning

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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Virtual sample generation method based on generative adversarial fuzzy neural network

Cui

Xia

Qiao

et al. 2022

Neural Comput & Applic

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Design of modeling error PDF based fuzzy neural network for effluent ammonia nitrogen prediction

Cited by 12 publications

References 39 publications

Short-term prediction of ammonia levels in goose houses via combined feature selector and random forest

Short-term prediction of ammonia levels in goose houses via combined feature selector and random forest

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

Virtual sample generation method based on generative adversarial fuzzy neural network

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