基于特征构造预处理与TCN-BiGRU的池塘溶解氧预测模型

为实现对池塘溶解氧变化趋势的准确和可靠预测，降低池塘养殖风险，提出了一种基于特征构造预处理（Feature construction,FC）与时间卷积网络（Temporal convolutional network,TCN）-双向门控循环单元（Bidirectional gate recurrent unit,BiGRU）的溶解氧预测模型FC-TCN-BiGRU。通过对样本序列的统计特征、环境因子特征和季节特征进行构造，挖掘变量间深层次的相关性；采用TCN对构造特征序列进行多层卷积和降维处理，在保留全局时序特征的同时去除冗余信息；结合BiGRU对降维特征进行建模，实现对溶解氧的准确预测。另外，构建了沙猫群算法（Sand cat swarm optimization,SCSO）优化的非参数核密度估计（Kernel density estimation,KDE）对溶解氧预测误差的分布范围进行估计。实验结果表明，模型的均方误差、平均绝对误差、均方根误差和决定系数分别为0.027 5、0.143 2、0.165 8和0.94，优于其他比较模型。同时，区间估计能够有效覆盖溶解氧的波动范围，量化...

Prediction model of dissolved oxygen in pond based on feature construction pretreatment and TCN-BiGRU

In order to achieve accurate and reliable prediction of the dissolved oxygen in ponds and mitigate aquaculture risks, we propose a predictive model based on Feature Construction (FC) pretreatment and Temporal Convolutional Network (TCN) coupled with Bidirectional Gate Recurrent Unit (Bi-GRU). By constructing statistical features, environmental factor features and seasonal features from the samples, deep-level correlations between variables are explored. Then, the structural feature sequences are processed using multiple layers of convolution and dimensionality reduction through TCN, while preserving the global temporal characteristics and removing redundant information. By integrating Bi-GRU to model the reduced features, accurate prediction of dissolved oxygen levels is achieved. Furthermore, the Sand Cat Swarm Optimization (SCSO) algorithm is employed to optimize the non-parametric Kernel Density Estimation (KDE) for estimating the distribution range of dissolved oxygen prediction errors. The experimental results indicate that the proposed model achieves superior performance compared to other comparative models, with respective values of 0.0275 for Mean Squared Error (MSE), 0.1432 for Mean Absolute Error (MAE), 0.1658 for Root Mean Squared Error (RMSE), and 0.94 for the Coefficient of Determination (R^2). Meanwhile, the interval estimation effectively covers the fluctuation range of dissolved oxygen, thereby quantifying the uncertainty in the prediction process. In the short-term prediction of dissolved oxygen levels in ponds, this model demonstrates notable accuracy and robustness. It is instructive for water quality monitoring in aquaculture and the enhancement of farming efficiency.