A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture
A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture
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DOI：
10.3390/s19061420
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发表时间：
2019-03-02
期刊：
影响因子：
3.9
通讯作者：
Liu, Juntao
中科院分区：
文献类型：
--
作者：
Hu, Zhuhua;Zhang, Yiran;Liu, Juntao
网箱养殖水质的准确预测是智能海水养殖的一个热点问题。由于海水养殖环境是开放的，水质参数的变化通常是非线性的，动态的，多变的，复杂的。然而，传统的预测方法存在预测精度低、泛化能力差、时间复杂度高等问题。为了解决这些不足，提出了一种基于深度LSTM（长短期记忆）学习网络的水质预测方法，用于预测pH和水温。首先，采用线性插值、平滑和移动平均滤波技术分别对水质数据进行修复、校正和去噪。其次，利用Pearson相关系数，得到pH、水温等水质参数之间的相关先验。最后，利用预处理后的数据及其相关信息，构建了基于LSTM的水质预测模型。实验结果表明，在短期预测中，pH值和水温的预测准确率可达98.56%和98.97%，预测时间开销分别为0.273 s和0.257 s。在长期预测中，pH和水温的预测精度分别达到95.76%和96.88%。
An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson's correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.