上海海洋大学学报2026，Vol.35Issue(1)：105-118,14.DOI:10.12024/jsou.20241204730
基于FC-TCN-GRU模型的凡纳滨对虾养殖水中氨氮和化学需氧量的预测
Prediction of ammonia nitrogen and chemical oxygen demand in Litopenaeus vannamei aquaculture ponds based on the FC-TCN-GRU model
摘要
Abstract
Based on water quality data from Litopenaeus vannamei aquaculture ponds in the same aquaculture farm during 2014-2018 and 2021-2024,this study selected key water quality parameters including total nitrogen(TN),total phosphorus(TP),active phosphorus(AP),nitrate nitrogen(NO3--N),nitrite nitrogen(NO2--N),total ammonia nitrogen(TAN),chemical oxygen demand(COD),temperature(T),and pH values to develop water quality prediction models for TAN and COD using temporal convolutional network(TCN)and gated recurrent unit(GRU).A hybrid FC-TCN-GRU model architecture was constructed,which employed TCN for feature extraction and dimensionality reduction of data features,fed the processed data into GRU,and finally maped the results through fully connected layers(FC)to generate predictions.Mean absolute error(MAE),mean squared error(MSE),and coefficient of determination(R2)values of the FC-TCN-GRU model for TAN prediction were 0.255,0.089 and 0.861,respectively,while achieved 1.750,4.840 and 0.332 for COD prediction.Compared with PCA-LSTM,basic LSTM and basic GRU models,the FC-TCN-GRU model showed better predictive accuracy for both TAN and COD prediction.The model performs superior in TAN prediction,but it still needs improvement in COD prediction.关键词
凡纳滨对虾/水质预测/全连接层/门控循环单元/时域卷积网络Key words
Litopenaeus vannamei/water quality prediction/fully connected layers/gate recurrent unit/temporal convolutional network分类
农业科技引用本文复制引用
王智华,吴昊,周英娴,李桂娟,江敏..基于FC-TCN-GRU模型的凡纳滨对虾养殖水中氨氮和化学需氧量的预测[J].上海海洋大学学报,2026,35(1):105-118,14.基金项目
上海市现代农业产业技术体系建设项目(沪农科产字[2022]第5号) （沪农科产字[2022]第5号）
