基于LightGBM-SSA-ELM的新疆羊舍CO2浓度预测

为减少肉羊集约化养殖过程中因环境恶化产生的应激反应,精准调控CO2质量浓度,提出了基于分布式梯度提升框架(LightGBM)、麻雀搜索算法(SSA)融合极限学习机(ELM)的CO2质量浓度预测模型.首先利用LightGBM筛选出与CO2质量浓度相关的重要特征,降低预测模型的输入维度;然后选择Sigmoid为激活函数,使...

Prediction of CO2 Concentration in Xinjiang Breeding Environment of Mutton Sheep Based on LightGBM-SSA-ELM

Air quality plays an important role in mutton sheep breeding environment, in order to reduce the stress response of CO2 to the growth of large-scale mutton sheep and ensure the healthy growth of mutton sheep in the appropriate environment, the key is to accurately control the CO2 in the mutton sheep breeding environment. A CO2 prediction model of mutton sheep breeding environment was proposed based on light gradient boosting machine (LightGBM), sparrow search algorithm (SSA) and extreme learning machine (ELM). Firstly, LightGBM was used to screen out the important characteristics of carbon dioxide concentration and reduce the input dimension of the prediction model. Then, ELM neural network algorithm with single hidden layer with strong nonlinear processing ability was used to build the CO2 prediction model. Finally, through the sparrow intelligent optimization algorithm, the super parameters needed in ELM model were optimized to obtain the best prediction model. The prediction model was applied to a large-scale mutton sheep breeding base in Manas County, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region, and good prediction results were obtained. The experimental results showed that the prediction model had good prediction effect, and the root mean square error (RMSE) of ELM was higher than that of SVR, BPNN, LSTM, GRU and LightGBM. The RMSE, mean absolute error (MAE) and R2 were 0.0213mg/L, 0.0136mg/L and 0.9886, respectively. The results showed that the combined model can not only achieve accurate control of carbon dioxide in sheep house, but also meet the needs of fine decision-making for mutton sheep breeding. It also can help farmers make decisions and reduce farming risks.