基于BOA – BiLSTM的鱼菜菇共生温室二氧化碳浓度预测模型

Prediction of Carbon Dioxide Concentration in Aquaponic-mushroom Greenhouse Based on BOA – BiLSTM Model

摘要:

二氧化碳(CO2)浓度是影响鱼菜菇共生系统生态循环与作物产量的关键参数,其精准预测对温室环境的智能调控具有重要意义。针对温室CO2浓度受多重环境因子非线性耦合影响导致预测精度不足、模型超参数难以实现自主高效寻优的问题,提出一种基于贝叶斯优化算法(Bayesian optimization algorithm,BOA)与双向长短期记忆网络(Bidirectional long short-term memory,BiLSTM)相结合的混合预测模型(BOA – BiLSTM)。该方法首先结合主成分分析(Principal component analysis,PCA)与农业系统动力学机制,筛选出鱼菜区温度、培养基温度、pH值等5个核心驱动因子作为输入特征;随后通过BOA算法的自适应寻优机制,对BiLSTM模型的关键超参数进行全局自动优化,有效克服了陷入局部最优的缺陷。试验结果表明,BOA – BiLSTM模型在CO2浓度单步预测中表现优异,其决定系数、均方根误差和平均绝对误差分别达到0.982和7.217、5.739μmol/mol。相较于基线模型,该模型不仅在多步预测中展现出更高的抗衰减稳定性,更在温室CO2异常突变区间的阈值事件识别中取得显著优势,其F1分数高达0.926,且模型平均推理时间仅为0.726ms。该方法为鱼菜菇共生温室环境的高效异常预警与精细化管理提供了兼具高精度与高实用性的技术支撑。

Abstract:

Carbon dioxide (CO2) concentration is a critical parameter influencing the ecological cycle and crop yield in aquaponic-mushroom symbiotic greenhouses, yet its accurate prediction remains challenging due to the highly nonlinear and strongly coupled multi-environmental factors. To address the low prediction accuracy and difficulty in achieving autonomous and efficient hyperparameter optimization of existing models, a hybrid prediction model (BOA – BiLSTM) was proposed integrating the Bayesian optimization algorithm (BOA) with a bidirectional long short-term memory (BiLSTM) network. Initially, principal component analysis (PCA) combined with agricultural system dynamics was employed to select five core driving factors, involving aquaponic area temperature, substrate temperature, pH value, salinity, and atmospheric pressure as input features, thereby reducing data redundancy and significantly enhancing model interpretability. The BOA was then utilized to automatically optimize the critical hyperparameters of the BiLSTM model. Comparative experiments on optimization algorithms revealed that, compared with genetic algorithm ( GA), particle swarm optimization ( PSO), and hippopotamus optimization (HO), the BOA-optimized model achieved maximum reductions of 27.67% and 24.17% in mean absolute error (MAE) and root mean square error (RMSE), respectively, alongside a maximum of 152.63% improvement in the coefficient of determination (R2). Furthermore, the BOA – BiLSTM model was comprehensively evaluated across multiple prediction horizons (2 min, 10 min, 30 min, and 1 h) against baseline models, including temporal convolutional network-Transformer (TCN-Transformer), bidirectional long short-term memory gated recurrent unit ( BiLSTM-GRU), convolutional neural network recurrent neural network (CNN-RNN), and mind evolutionary algorithm back propagation neural network (MEA-BP). In 2-min single-step prediction, the proposed model demonstrated superior performance with an R2, RMSE, and MAE of 0.982, 7.217 μmol/ mol, and 5.739 μmol/ mol, respectively, while exhibiting remarkable anti-decay stability in multi-step forecasting tasks. Ablation studies among LSTM, BiLSTM, BOA LSTM, and BOA – BiLSTM architectures further confirmed that the integration of the bidirectional structure and BOA global optimization significantly mitigated local optima traps and maximized feature extraction synergy. Practically, the model secured a substantial advantage in identifying threshold exceedance events during abnormal CO2 mutations with an F1 score of 0.926, maintaining a low average inference time of 0.726 ms. Finally, the dominant factors driving CO2 changes were revealed to be pH value and aquaponic temperature through the integral gradient method, in order to improve the interpretability of the model. This approach can provide a highly accurate, robust, and deployable technical foundation for intelligent environmental regulation and precision management in symbiotic greenhouse facilities.