Machine Learning and Wavelet Transform: A Hybrid Approach to Predicting Ammonia Levels in Poultry Farms

Ammonia (NH₃) is a major pollutant in poultry farms, negatively impacting bird health and welfare. High NH₃ levels can cause poor weight gain, inefficient feed conversion, reduced viability, and financial losses in the poultry industry. Therefore, accurate estimation of NH₃ concentration is crucial for environmental protection and human and animal health. Three widely used machine learning (ML) algorithms-extreme learning machine (ELM), k-nearest neighbor (KNN), and random forest (RF)-were initially used as base algorithms. The wavelet transform (WT) with ten levels of decomposition was then applied as a preprocessing method. Three statistical metrics, including the mean absolute error (<i>MAE</i>) and the correlation coefficient (<i>R</i>), were used to evaluate the predictive accuracies of algorithms. The results indicate that the RF algorithms perform robustly individually and in combination with the WT. The RF-WT algorithm performed best using the air temperature, relative humidity, and air velocity inputs with a <i>MAE</i> of 0.548 ppm and an <i>R</i> of 0.976 for the testing dataset. In summary, applying WT to the inputs significantly improved the predictive power of the ML algorithms, especially for inputs that initially had a low correlation with the NH₃ values.