A prediction model of NH3 concentration for swine house in cold region based on Empirical Mode Decomposition and Elman neural network

In order to improve the accuracy and reliability of ammonia (NH3) concentration prediction, which can provides a support to the ventilation control strategy, so as to reduce the impact of NH3 on the health and productivity of swine, this paper proposed an NH3 concentration prediction method based on Empirical Mode Decomposition (EMD) and Elman neural network modelling. The NH3 concentration and other four environmental parameters including temperature, humidity, carbon dioxide and light intensity were decomposed into several different time-scale intrinsic mode functions (IMFs). Then, the Elman neural network prediction model was used to predict each IMF. The predicted NH3 was obtained by reconstructing all the IMFs by EMD. The results show that for the proposed method, the determination coefficient between the predicted and real measured value is 0.9856, the Mean Absolute Error is 0.7088 ppm, the Root Mean Square Error is 0.9096 ppm, and the Mean Absolute Percentage Error is 0.41%. Compared with the Elman neural network, the proposed method has a good improvement in the accuracy, and provide effective parameters for the environmental monitoring of the swine house and the regulation of the NH3 concentration.