基于PC-RELM的养殖水体溶解氧数据流预测模型

养殖水体中溶解氧浓度一直是最重要的水质参数之一。为了精准地对水体溶解氧进行调控,提高养殖生产效率,降低养殖风险,该研究考虑外部天气条件对溶解氧的影响以及溶解氧自身的昼夜变化特征,提出一种基于正则化极限学习机（principal component analysis and clustering method optimized regularized extreme learning machine,PC-RELM）的养殖水体溶解氧数据流预测模型。首先,采用主成分分析法判断影响溶解氧浓度的强重要性因子,降低预测模型的数据维度;其次,利用熵权法计算各时刻点的天气环境指数,并利用快速动态时间规整算法（fast dynamic time warping,FastDTW）完成时间序列数据流在不同天气环境下的相似度度量;然后使用k-means算法对时间序列的相似度进行聚类分簇,并基于分簇结果完成正则化极限学习机预测模型的构建,实现溶解氧浓度的估算。最后将PC-RELM模型应用到无锡南泉试验基地养殖池塘的溶解氧预测调控过程中。试验结果表明：PC-RELM的预测均方根误差值（root mean sq...

Data stream prediction model for dissolved oxygen in aquaculture water using PC-RELM

Dissolved oxygen (DO) is one of the most important parameters for the water quality in aquaculture water. Long-term low oxygen environment can dominate the growth and reproduction of fish. Hypoxia can also cause large areas of fish death. Accurate and efficient DO prediction and control strategies can improve aquaculture production efficiency for the fewer aquaculture risks. However, an effective DO prediction has always been a tough challenge in aquaculture, due to the interference of external weather and the DO complexity. Multi-source or single sensors are generally used to build the prediction models, without considering the DO characteristics under similar weather conditions. Particularly, there is an outstanding diurnal variation in the DO content. Moreover, some redundant data can be collected from the water quality sensors in automatic weather stations. In this study, the principal component analysis and clustering method optimized regularized extreme learning machine (PC-RELM) was proposed to realize the DO prediction, considering the influence of external weather conditions on the DO and the diurnal variation. Firstly, the principal component analysis (PCA) was applied to determine the most influencing factors on the DO concentration, and reduce the data dimension of the prediction model for the high efficiency of prediction; Secondly, the entropy weight method was utilized to calculate the weather environment index at different time points. Fast dynamic time warping (FastDTW) was used to measure the similarity of weather environment in the time series data streams; Then, the K-means algorithm was used to cluster the similarity of the time series using the weather environment index. And the sub-prediction models of regularized extreme learning machine (RELM) were constructed using the clustered datasets to forecast the DO concentration. Finally, the PC-RELM model was applied to the intelligent control process of DO in the aquaculture pond of the Wuxi Nanquan experimental base. The test results showed that the root-mean square error (RMSE) of PC-RELM prediction was 0.961 9, which outperformed the partial least squares optimized ELM (PLS-ELM), Least Square Support Vector Machine (LSSVM), and BP algorithms by 41.54%, 54.58%, and 67.16%, respectively. The mean square error (MSE) value of PC-RELM was 0.6941, which outperformed the PLS-ELM, LSSVM and BP algorithms by 46.26%, 59.98%, and 69.90%, respectively. Meanwhile, the Nash-Sutcliffe efficiency coefficient of PC-RELM was 0.712 8, which was much higher than the rest prediction. In addition, the PC-RELM presented a high running speed of 0.316 2 s. The efficiency of PC-RELM was improved by about 7, 10, and 40 times, respectively, compared with the PLS-ELM, LSSVM, and BP. The improved model can be expected to extract the change patterns of DO under different weather conditions, indicating high prediction accuracy and efficiency. The finding can provide high-quality data and theoretical support for the precise control of DO in the pond water.