基于局部化双向LSTM和状态转移约束的养殖水质分类预测
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摘要
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.
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.
引文
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[3] 刘玉梅,张长利,王树文,等.基于ZigBee技术的水产养殖环境监测系统设计[J].自动化技术与应用,2011,30(3):50-53.
[4] 赵景波,薛秉鑫.基于粒子群的模糊神经网络养殖池塘溶氧预测研究[J].渔业现代化,2018,45(4):8-14.
[5] 李海涛,茆毓琦.基于启发式Johnson算法优化BP神经网络的水产养殖产量预测模型[J].渔业现代化,2017,44(6):19-23.
[6] 马从国,赵德安,王建国,等.基于无线传感器网络的水产养殖池塘溶解氧智能监控系统[J].农业工程学报,2015,31(7):193-200.
[7] 胡海清,周小丽,宋毅.LM-BP 神经网络在水质预测的应用[J].微型电脑应用,2011,27(9):44-46.
[8] 袁琦,黄建清,符新.基于神经网络的水产养殖水质预测模型研究[J].湖北农业科学,2013,52(1):143-146.
[9] 丁金婷,臧泽林,黄敏.模糊方法改进的反向传输神经网络预测南美白对虾养殖的水质[J].浙江大学学报(农业与生命科学版),2017,43(1):128-136.
[10]商志根,段小汇.基于PSO与LS-SVM的作物需水量预测[J].计算机与现代化,2018,27(10):44-47.
[11]任君,王传美,王建祥.基于正则化LSTM模型的股票指数预测[J].计算机应用与软件,2018,35(4):44-48.
[12]王秀美,牟少敏,邹宗峰,等.基于深度学习的小麦蚜虫预测预警[J].江苏农业科学,2018,46(5):183-187.
[13]姚俊杨,许继平,王小艺,等.基于深度学习的湖库藻类水华预测研究[J].计算机与应用化学,2015,32(10):1265-1268.
[14]谭娟,王胜春.基于深度学习的交通拥堵预测模型研究[J].计算机应用研究,2015,32(10):2951-2954.
[15]GRAVES A,SCHMIDHUBER J.Framewise phoneme classification with bidirectional LSTM and other neural network architectures [J].Neural Network,2005,18(5):602-610.
[16]HOCHREITER S,SCHMIDHUBER J.Long short-term memory [J].Neural Computation,1997,9(8):1735-1780.
[17]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors [J].Nature,1986,323(6088):399-421.
[18]朱刘影,杨思春.基于SVM 的地理试题自动分类[J].计算机应用研究,2018,35(9):2707-2710.
[19]刘方园,王水花,张煜东.支持向量机模型与应用综述[J].计算机系统应用,2018,27(4):1-9.
[20]罗帆,王厚峰.结合RNN 和CNN 层次化网络的中文文本情感分类[J].北京大学学报(自然科学版),2018,54(3):459-465.
[21]ZHANG J,ZHU Y,ZHANG X,et al.Developing a long short-term memory(LSTM) based model for predicting water table depth in agricultural areas [J].Journal of Hydrology,2018,561:918-929.
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