| 采用改进长短时记忆神经网络的水产养殖溶解氧预测模型 |
| |
| 引用本文： | 曹守启,周礼馨,张铮.采用改进长短时记忆神经网络的水产养殖溶解氧预测模型[J].农业工程学报,2021,37(14):235-242. |
| |
| 作者姓名： | 曹守启 周礼馨 张铮 |
| |
| 作者单位： | 1.上海海洋大学工程学院,上海 201306;2.上海海洋可再生能源工程技术研究中心,上海 201306 |
| |
| 基金项目： | 十三五"蓝色粮仓科技创新"国家重点研发计划项目（2019YFD0900800）;上海市工程技术研究中心建设计划"上海海洋可再生能源工程技术研究中心"（编号19DZ2254800） |
| |
| 摘 要： | 为了精确预测水产养殖溶解氧变化趋势,该研究提出了基于K-means聚类和改进粒子群优化(Improved Particle Swarm Optimization,IPSO)的长短时记忆(Long Short-term Memory,LSTM)神经网络预测模型。根据环境因子间的相似度,应用改进的K-means聚类算法将环境数据划分为若干类。在此基础上,基于LSTM神经网络算法构建改进的水产养殖溶解氧预测模型,并引入改进粒子群优化算法对模型参数进行优化,以减少经验选取参数的盲目性。在不同天气状况下利用该模型对溶解氧进行预测。试验结果表明,在良好天气情况下,该模型预测误差曲线波动较小,预测精度更高。当天气发生突变时,溶解氧预测模型评价指标平均绝对百分误差、均方根误差、平均绝对误差和纳什系数分别为0.129 5、0.645 3、0.461 3和0.902 2。该模型一定程度改善了天气突变状况下的数据缺失、鲁棒性差等问题。
|
| 关 键 词： | 水产养殖 水质 溶解氧 聚类 LSTM 粒子群优化 |
| 收稿时间： | 2021/5/18 0:00:00 |
| 修稿时间： | 2021/7/18 0:00:00 |
Prediction model of dissolved oxygen in aquaculture based on improved long short-term memory neural network |
| |
| Cao Shouqi,Zhou Lixin,Zhang Zheng.Prediction model of dissolved oxygen in aquaculture based on improved long short-term memory neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(14):235-242. |
| |
| Authors: | Cao Shouqi Zhou Lixin Zhang Zheng |
| |
| Abstract: | Abstract: Dissolved oxygen (DO) has become an important parameter to predict water quality in modern aquaculture. The excessive or insufficient DO content in the water has posed a great impact on the metabolism and physiological functions of breeding organisms, even a threat to normal growth. However, DO is also characterized by nonlinear, large inertia, strong coupling, and time-varying, while easily affected by many factors, such as weather, water quality, and human activities. Therefore the DO prediction is highly demanding for the reduction and prevention of aquaculture disaster in safe agricultural production. In this study, a hybrid DO prediction model was proposed in aquaculture using K-means clustering and improved long short-term memory neural network (KLSTM). Meanwhile, the improved particle swarm optimization (IPSO) was introduced to optimize the parameter selection of the model, thereby predicting the change of DO in aquaculture from the perspective of time series. The similarity among variables was calculated, according to the weight of influencing factors under different weather conditions. K-means clustering was then applied to divide the dataset into multiple clusters. The periodic changes and trends of variables were determined to optimize the selection of training samples, while reducing the running time of the whole model. An LSTM-DO prediction model was then established for aquaculture using deep learning frameworks. The KLSTM-PSO prediction curve was closer to the actual one than others, indicating a higher prediction accuracy. The experimental results show that the prediction error of the model fluctuated less, while the prediction accuracy was higher in good weather conditions. The MAPE, RMSE, MAE, and NSC of the proposed model were 0.1295, 0.6453, 0.4613, and 0.9022, respectively, when the weather changed suddenly. The best NSC performance was found in the IPSO-KLSTM neural network among the six models. The NSC of IPSO-KLSTM was 1.62% higher than that of PSO-KLSTM, and 4.17% higher than that of PSO-LSSVM, indicating a higher prediction performance of the model with the optimized selection of samples and parameters. Similarly, the average RMSE of the model was improved by 17.10%, 24.89%, and 24.21%, respectively, compared with the single LSTM, ELM, and BP models, indicating a higher tolerance to the anomaly or missing sensor data caused by uncertain mixed weather conditions. Correspondingly, the LSTM neural network was an effective way to predict DO content in the intensive aquaculture, further to balance the stability and accuracy of prediction. Therefore, the IPSO-KLSTM was widely expected to serve as the optimal sample selection, with the low interference between different samples under the weather conditions, and high tolerance to emergencies for a higher prediction accuracy. The finding can offer a highly accurate prediction framework to track the dissolved oxygen in modern aquaculture. |
| |
| Keywords: | aquaculture water quality dissolved oxygen clustering improved PSO long short-term memory |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
