Title: A prediction model for piggery ammonia concentration based on least squares support vector regression using fruit fly optimisation algorithm

Authors: Chong Chen; Xingqiao Liu

Addresses: College of Electrical and Information Engineering, Jiangsu University, Zhenjiang Shi, Jiangsu Sheng, China; College of Electrical Engineering, Yancheng Institute of Technology, Yancheng Shi, Jiangsu Sheng, China ' College of Electrical and Information Engineering, Jiangsu University, Zhenjiang Shi, Jiangsu Sheng, China; National Engineering and Technology Centre for Information Agriculture, Nanjing Agriculture University, Nanjing, Jiangsu, China

Abstract: In order to predict the variation trend of ammonia (NH₃) concentration accurately in piggery and reduce the risk of livestock breeding, a prediction model is established. Because NH₃ has a great influence on the health of pigs, a prediction model can provide an effective way for pig industries to determine the environmental control strategy and take effective measures to evaluate the air quality of piggery. When predicted value of NH₃ concentration is above the warning value, farmers can start fans in advance to maintain the health of pigs. The proposed NH₃ concentration prediction model is based on Least Squares Support Vector Regression (LSSVR) model with Fruit Fly Optimisation Algorithm (FOA) to search the optimal parameters γ and σ of LSSVR. As the performances of LSSVR are greatly affected by the two parameters, three optimisation algorithms, Particle Swarm Optimisation (PSO) algorithm, Genetic Algorithm (GA) and traditional LSSVR, are used to compare with FOA. The calculated mean absolute percentage errors of the four prediction models are 0.81%, 2.95%, 4.04% and 5.92%, respectively. The prediction model is used in livestock breeding base, Zhenjiang City, China, and it performs well. The FOA-LSSVR prediction model can serve as an effective strategy applied in multivariable and non-linear piggery environmental control system.

Keywords: ammonia concentration; prediction model; LSSVR; least squares support vector regression; FOA; fruit fly optimisation algorithm; parameter optimisation.

DOI: 10.1504/IJWMC.2019.101027

International Journal of Wireless and Mobile Computing, 2019 Vol.17 No.1, pp.54 - 62

Received: 04 Jul 2018
Accepted: 07 Feb 2019
Published online: 22 Jul 2019 *

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