| 陈静,李政委,杨凯,凌冰心,李政权,祝家福.基于IGJO-TCN-BiGRU-SA的水质预测模型[J].电子测量与仪器学报,2026,40(4):23-39 |
| 基于IGJO-TCN-BiGRU-SA的水质预测模型 |
| Water quality prediction model based on IGJO-TCN-BiGRU-SA |
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| DOI： |
| 中文关键词: 水质预测 双向门控循环单元 时域卷积网络 自注意力机制 改进的金豺优化算法 三次样条插值 |
| 英文关键词:water quality prediction bidirectional gated recurrent unit temporal convolutional network self-attention mechanism improved golden jackal optimization cubic spline interpolation |
| 基金项目:国家自然科学基金项目（51874010）、安徽省教育厅高校自然科学研究项目（KJ2018A0087）、安徽理工大学2025年大学生创业基金扶持项目、安徽理工大学创新基金项目（2025cx2083）资助 |
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| Author | Institution |
| Chen Jing | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Li Zhengwei | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Yang Kai | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Ling Bingxin | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China |
| Li Zhengquan | Aviation Maintenance NCO School, Air Force Engineering University, Xinyang 464000, China |
| Zhu Jiafu | School of Materials Science and Engineering, Anhui University of Science and Technology, Huainan 232001，China |
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| 中文摘要: |
| 针对水质数据的时序性、 强非线性及维度高导致模型预测精度低的问题, 提出一种基于IGJO-TCN-BiGRU-SA的水质预测模型。 首先, 通过三次样条插值 (cubic spline interpolation, CSI) 算法修复缺失数据, 采用箱线图 (Boxplot) 和快速傅里叶变换 (fast Fourier transformation, FFT) 算法剔除异常数据, 提升数据的完备性, 利用灰色关联分析 (grey relational analysis, GRA) 算法分析水质指标相关性, 得到强相关性的水质指标; 其次, 以双向门控循环单元 (bidirectional gated recurrent unit, BiGRU) 为基准模型, 提取水质数据中的潜在变化特征, 采用时域卷积网络 (temporal convolutional network, TCN) 对数据进行局部特征提取, 增强了模型对不同时间尺度特征的提取能力, 并通过自注意力机制 (self-attention, SA) 自适应更新BiGRU模型特征权重, 有效提取全局上下文信息的关键特征; 最后采用改进的金豺优化算法 (improved golden jackal optimization, IGJO) 寻优TCN的L2参数、 BiGRU的学习率和神经元个数、 SA机制的键值, 从而提升水质预测精度。 以淮河流域水质历史数据为样本进行实验, 相对于LSTM、GRU、BiGRU和TCN-BiGRU-SA等经典算法, 所提出的IGJO-TCN-BiGRU-SA预测算法在MAE上降低19.64%~68.29%, RMSE上降低25.47%~75.19%, MAPE上降低28.71%~73.36%, R2上提升0.19%~3.61%。 实验结果证明, 基于IGJO-TCN-BiGRU-SA的预测算法明显提高了水质预测精度。 |
| 英文摘要: |
| Aiming at the problems of low model prediction accuracy caused by the temporality, strong nonlinearity, and high dimensions of water quality data, a water quality prediction model based on the IGJO-TCN-BiGRU-SA has been proposed. Firstly, missing data are repaired through the CSI algorithm, while abnormal data are removed through Boxplot and FFT algorithms to enhance data completeness. The GRA algorithm is used to analyze the correlation of water quality indicators and obtain strongly correlated water quality indicators. Secondly, the BiGRU model is employed as the baseline model to extract latent change features from the data. The TCN algorithm is employed for local feature extraction from the data, enhancing the model’s capability to extract features at different time scales. The SA mechanism is used to adaptively update the feature weights of the BiGRU model, effectively extracting key features from global contextual information. Finally, the IGJO algorithm is employed to optimize the L2 of TCN, the learning rate and neuron number of BiGRU, and the key of the SA mechanism, thereby improving the accuracy of water quality prediction. The historical water quality data from the Huai River Basin were used as samples for experiments. Compared to classic algorithms such as LSTM, GRU, BiGRU and TCN-BiGRU-SA, the IGJO-TCN-BiGRU-SA prediction algorithm proposed in this research achieved reductions of 19.64% to 68.29% in MAE, 25.47% to 75.19% in RMSE, 28.71% to 73.36% in MAPE, and 0.19% to 3.61% in R2. Experimental results indicate that the water quality prediction algorithm based on IGJO-TCN-BiGRU-SA significantly enhances the model’s predictive accuracy. |
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