基于局部化双向LSTM和状态转移约束的养殖水质分类预测

Aquaculture water quality prediction based on local Bi-LSTM and state transformation constraint

养殖水质对水产养殖的产出和收益具有非常重要的影响,提前预测水质状况可以提高治理水平,从而减少水产养殖的损失。以南美白对虾(俗称白肢虾)日常养殖水质为研究对象,选取温度(T)、pH、溶氧(DO)、盐度、氧化还原电位(ORP)、亚硝酸盐氮(NO^-_2-N)和氨氮(NH^+_4-N)作为水质数据特征,提出基于局部化双向LSTM(CovBiLSTM)和状态转移约束的水质预测模型(CovBiLSTMST)。首先使用双向LSTM网络接收历史水质数据序列信息的输入,然后利用卷积函数和最大池化技术(Max pooling)来挖掘双向LSTM网络不同单元输出数据之间的关系,对历史不同时间的水质数据进行融合,最后采用Softmax分类器来预测水质状况,同时利用状态转移约束条件来提高预测的准确率。通过对比试验,分别证明了局部双向化LSTM和状态转移约束对水质预测的有效性。与基于LSTM的预测方法相比,评价指标分类准确率和召回率分别提高5%和4%。研究表明:基于局部化双向LSTM(CovBiLSTM)网络模型的水质预测算法比基于其他模型的预测算法能更准确预测南美白对虾的水质状况。

Aquaculture water quality has a very important impact on the output and income of aquaculture.Predicting the water quality state in advance can improve the governance level and thus reduce the loss of aquaculture.With daily aquaculture water quality of Penaeus vannamei (known as white-leg shrimp) as the object of study,and the temperature(T),pH,dissolved oxygen(DO),salinity,oxidation reduction potential(ORP),NO - 2^-N and NH + 4^-N selected as water quality data,a water quality prediction model based on local Bi-LSTM(CovBiLSTM) and state transformation constraint is proposed.First,Bi-LSTM is used to receive the input historical water quality data sequence information,then the convolution function and max pooling are used to mine the relationship between the output data of different units in Bi-LSTM for integration of water quality data in different historical periods,and finally Softmax classifier is used to predict the water quality state and the state transformation constraint is used to improve the accuracy of prediction.The effectiveness of local Bi-LSTM and state transformation constraint in water quality prediction is demonstrated by comparative experiments.Compared with the prediction method based on LSTM,the classification accuracy and recall rate of evaluation indexes are improved by 5% and 4% respectively.The results show that the water quality prediction algorithm based on CovBiLSTM network model can predict the water quality state of Penaeus vannamei more accurately than that based on other models.