养殖水体溶解氧含量预测研究  

Research on the prediction of dissolved oxygen content in aquaculture water

作　　者：徐慧[1] 邓浩然 王忠培 周乐乐 钱蓉 XU Hui;DENG Haoran;WANG Zhongpei;ZHOU Lele;QIAN Rong(School of Information and Artificial Intelligence,Wuhu Institute of Technology,Wuhu 241006,Anhui;Institute of Agricultural Economics and Information,Anhui Academy of Agricultural Sciences,Hefei 230031,Anhui)

机构地区：[1]芜湖职业技术学院信息与人工智能学院,安徽芜湖241006 [2]安徽省农业科学院农业经济与信息研究所,安徽合肥230031

出　　处：《浙江农业科学》2025年第1期176-180,共5页Journal of Zhejiang Agricultural Sciences

基　　金：芜湖职业技术学院校级自然科学重点研究项目(wzyzrzd202313);安徽省财政农业科技成果转化项目(2024ZH009)。

摘　　要：溶解氧作为水产养殖中较为关键的水质因子,与水产品的产量和质量息息相关,精准预测溶解氧含量及变化,对于保证水产养殖的安全具有较大的意义。该研究首先对采集的养殖水体的溶解氧数据进行预处理,再结合长短时记忆网络(LSTM)算法构建养殖水体溶解氧含量的预测模型来预测未来不同时刻的溶解氧浓度数据,通过不同的预测精度指标,来验证养殖水体溶解氧含量预测模型的预测精度,以期为后续养殖水体溶解氧含量预测的相关研究提供参考。Dissolved oxygen,as a key water quality factor in aquaculture,is closely related to the yield and quality of aquatic products,and accurate prediction of dissolved oxygen content and changes is of great significance for ensuring the safety of aquaculture.In this study,the dissolved oxygen data of the collected aquaculture water were preprocessed,and then combined with the long and short-term memory(LSTM)neural network algorithm,the prediction model of the dissolved oxygen content of the aquaculture water was constructed to predict the dissolved oxygen concentration data at different times in the future,and the prediction accuracy of the prediction model of the dissolved oxygen content of aquaculture water was verified through different prediction accuracy indicators,in order to provide reference for the subsequent research on the prediction of dissolved oxygen content in aquaculture water.

关 键 词：养殖水体 溶解氧含量 长短时记忆网络 预测模型 

分 类 号：S959[农业科学—水产养殖] TP183[农业科学—水产科学]