A Hybrid Model for Predicting the Environment Humidity of Pigeon Sheds

Abstract

Pigeon is a kind of poultry with high practicability and economic value, and humidity is an important indicator of the intensive breeding of pigeons, which is closely related to the healthy growth of pigeons. Humidity changes are complex, dynamic, and nonlinear. Accurately predicting humidity values and analyzing their changing trends is crucial to the growth of pigeons. Addressing low prediction accuracy and poor generalization of traditional humidity prediction methods, this paper proposes a combination based on partial least squares (PLS) data dimensionality reduction, Savitzky-Golay (SG) filter, and sparrow optimization algorithm (SSA) Predictive models to improve humidity forecast accuracy. First, the SG filter is used for data smoothing to remove abnormal noise in the data signal to enhance the feature learning ability of the prediction system. Next, the original data is selected through the PLS method to select information higher than the set threshold. Valuable information features are extracted for data dimensionality reduction. In addition, SSA is used to optimize key parameters and structure of the BiGRU model for improved prediction. Finally, a combined prediction model based on SG-PLS-SSA-BiGRU was built to simulate and predict the humidity of the pigeon house. The experimental results demonstrate the proposed model's high accuracy and stability with minimal prediction error fluctuations, fast calculation speed, good feature extraction ability and generalization ability, and is very effective and reliable for predicting pigeon house breeding humidity.