[1]任妮,鲍彤,刘杨,等.基于粒子群优化算法和长短时记忆神经网络的蟹塘溶解氧预测[J].江苏农业学报,2021,(02):426-434.[doi:doi:10.3969/j.issn.1000-4440.2021.02.020]
　REN Ni,BAO Tong,LIU Yang,et al.Prediction model of dissolved oxygen in Chinese mitten crab ponds based on particle swarm optimization algorithm and long short-term memory neural networks[J].,2021,(02):426-434.[doi:doi:10.3969/j.issn.1000-4440.2021.02.020]
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基于粒子群优化算法和长短时记忆神经网络的蟹塘溶解氧预测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年02期
页码:
426-434
栏目:
畜牧兽医·水产养殖
出版日期:
2021-04-30

文章信息/Info

Title:
Prediction model of dissolved oxygen in Chinese mitten crab ponds based on particle swarm optimization algorithm and long short-term memory neural networks
作者:
任妮1; 鲍彤1; 刘杨1; 荀广连1; 蒋永年2
(1.江苏省农业科学院农业信息研究所,江苏南京210014;2.江苏中农物联网科技有限公司，江苏宜兴214200)
Author(s):
REN Ni1; BAO Tong1; LIU Yang1; XUN Guang-lian1; JIANG Yong-nian2
(1.Institute of Agricultural Information，Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China;2.Jiangsu Zhongnong Internet of Things Technology Co., Ltd., Yixing 214200,China)
关键词:
溶解氧预测; 河蟹养殖; 粒子群优化算法; 长短时记忆神经网络
Keywords:
prediction of dissolved oxygen; culturing of Chinese mitten crab; particle swarm optimization algorithm; long short-term memory neural networks
分类号:
S126
DOI:
doi:10.3969/j.issn.1000-4440.2021.02.020
文献标志码:
A
摘要:
为准确预测蟹塘溶解氧质量浓度，及时掌握溶解氧质量浓度的变化趋势，提前采取防控措施从而降低河蟹养殖风险，提出了一种基于粒子群优化算法（PSO）和长短时记忆神经网络（LSTM）的蟹塘溶解氧质量浓度预测模型，采用PSO算法优化LSTM模型参数后对蟹塘溶解氧质量浓度进行预测。结果表明，PSO-LSTM模型不仅整体优于ARIMA模型，相较于其他LSTM模型也有更高的预测精度，在连续10个时间点的预测中相比于LDO-LSTM、LSTM和ARIMA模型平均百分误差分别降低了2.55%、1.891%和4.055%。说明PSO-LSTM模型在蟹塘溶解氧质量浓度预测中具有良好的准确性和稳定性，可以为河蟹养殖中水质精准预测与调控提供参考。
Abstract:
To predict the mass concentration of dissolved oxygen in Chinese mitten crab ponds accurately, grasp the changing trend of the mass concentration of dissolved oxygen timely and take preventive and control measures in advance to reduce the risk in Chinese mitten crab culturing, a model for predicting the mass concentration of dissolved oxygen in Chinese mitten crab ponds based on particle swarm optimization (PSO) and long short-term memory (LSTM) neural networks was proposed. The mass concentration of dissolved oxygen in Chinese mitten crab ponds was predicted after optimizing LSTM model parameters by PSO algorithm. The results showed that the PSO-LSTM model was not only superior to the ARIMA model, but also had higher prediction accuracy compared with other LSTM models. In the predictions at 10 consecutive time points, the average percentage error of the PSO-LSTM model reduced by 2.55%, 1.891% and 4.055% respectively, compared with the LDO-LSTM, LSTM and ARIMA models. It can be seen that the PSO-LSTM model has good accuracy and stability in the prediction of the mass concentration of dissolved oxygen in Chinese mitten crab ponds, and can provide a reference for accurate prediction and regulation of water quality in Chinese mitten crab culturing.

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备注/Memo

备注/Memo:
收稿日期：2020-09-14基金项目：江苏省农业科技自主创新基金项目[CX(19)1003]作者简介：任妮(1983-)，女，山东莱州人，博士，副研究员，研究方向为大数据分析和知识组织等。(E-mail)rn@jaas.ac.cn
更新日期/Last Update: 2021-05-10