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

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. A novel forecasting model was proposed by combining feature selector (CFS) and random forest (RF) to improve the prediction accuracy of NH₃ 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 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, and 2.88%, respectively. The NH₃ 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 NH₃ 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.