长短期记忆神经网络在叶绿素<i>a</i>浓度预测中的应用

石绥祥; 王蕾; 余璇; 徐凌宇

doi:10.3969/j.issn.0253-4193.2020.02.014

长短期记忆神经网络在叶绿素a浓度预测中的应用

doi: 10.3969/j.issn.0253-4193.2020.02.014

石绥祥^1,,
王蕾^{1, 2, ,},
余璇³,
徐凌宇³

1.
国家海洋信息中心数字海洋实验室，天津 300171
2.
国家海洋局东海信息中心，上海 200136
3.
上海大学计算机工程与科学学院，上海 200444

基金项目: 国家重点研发计划—“海洋环境安全保障”重点专项（2016YFC1401900，2016YFC1403200）；天津市企业博士后创新项目择优资助项目（TJQYBSH2018025）；国家海洋局东海分局青年科技基金（201615）。

详细信息

作者简介:
石绥祥（1966—），男，浙江省绍兴市人，研究员，从事海洋大数据分析预报技术研究。E-mail：shisuixiang@hotmail.com

通讯作者:
王蕾，博士，从事海洋大数据分析挖掘技术研究。E-mail：wangleidett727@163.com

中图分类号: X524
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- 被引次数: 0
出版历程
- 收稿日期: 2019-03-12
- 修回日期: 2019-06-28
- 网络出版日期: 2020-11-18
- 刊出日期: 2020-02-25

Application of long term and short term memory neural network in prediction of chlorophyll a concentration

Shi Suixiang^1
,,
Wang lei^{1, 2
, ,},
Yu Xuan³,
Xu Lingyu³

1.
Key Laboratory of Digital Ocean, National Marine Data and Information Service, Tianjin 300171, China
2.
East Sea Information Center of State Oceanic Administration, Shanghai 200136, China
3.
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

摘要

摘要: 针对传统人工神经网络对叶绿素a浓度预测存在训练速度慢、收敛精度低、易陷入局部最优，尤其是无法灵活的利用任意长度的历史信息对叶绿素a浓度进行预测等问题，本文根据海洋各要素与叶绿素a浓度之间的长短期依赖程度，对叶绿素a浓度与各要素间的关系进行界定，分别将各要素与叶绿素a浓度之间的长期依赖关系与短期依赖关系分割开来，并且在长短期记忆(Long Short-Term Memory, LSTM)神经网络模型的基础上构建融合的LSTM预测模型，模型中的长期依赖关系与短期依赖关系分别使用不同的神经元，最终在模型的最上层进行长短期融合。本文选取三都澳站位的连续监测资料作为实验数据，实验结果表明本文构建的模型不仅具有训练误差下降快的优点，与其他3种经典的神经网络模型相比，预测精度也有显著提高。
- 叶绿素a /
- 融合的LSTM预测模型 /
- 多要素 /
- 神经网络
Abstract: Prediction of chlorophyll a concentration in traditional artificial network methods has some disadvantages, such as slower training speed, lower convergence precision, and easy to fall into local optimum situation. In particular, it is not possible to flexibly use historical information of any length to predict chlorophyll a concentration. To solve these problems, this paper defines the relationship between chlorophyll a concentration and various elements, depending on the long-term and short-term dependence between elements and the concentration of chlorophyll a. In this way, the long-term dependence between each element and the chlorophyll a concentration is separated from the short-term dependence. Then, based on the Long Short-Term Memory (LSTM), a merged LSTM prediction model was proposed. In this model, short and long term dependencies were presented respectively by different neurons and finally merged at the top of the model. The experimental data involves the continuous monitoring data of the station of Sandu Ao. The main result includes that the model has the advantage of fast reduction of training error, but also has significantly higher prediction accuracy compared with other three classical neural network models.
- chlorophyll a /
- Merged-LSTM /
- multi-factors /
- neural network

HTML全文

图 1 三都澳站位分布

Fig. 1 Station location distribution map of Sandu Ao

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图 2 研究方法图

Fig. 2 Research method map

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图 3 LSTM神经元结构

Fig. 3 Neuron Structure of LSTM

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图 4 LSTM内部计算流程图

Fig. 4 LSTM internal calculation flow chart

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图 5 Merged-LSTM结构图

Fig. 5 The structure of Merged-LSTM

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图 6 要素间相似矩阵图

Fig. 6 Similarity matrix diagram between elements

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图 7 模型训练误差下降情况

Fig. 7 Decline of model training error

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图 8 实际叶绿素a浓度值和预测叶绿素a浓度值

actual为真实值，Merged-LSTM为本文所提出的模型，RNN为递归神经网络，MLP为多层感知器，Regression为回归方法

Fig. 8 Actual and predicted chlorophyll a concentration

actual represents the true value, Merged-LSTM is the proposed model, RNN is recursive neural network, MLP is multilayer perceptron, Regression is the regression method

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图 9 实验结果图

Merged-LSTM为本文所提出的模型，RNN为递归神经网络，MLP为多层感知器，Regression为回归方法

Fig. 9 Experimental results

Merged-LSTM is the proposed model, RNN is recursive neural network, MLP is multilayer perceptron, Regression is the regression method

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表 1 不同时延下相关系数

Tab. 1 Correlation coefficient under different time delays

	1	2	3	4	5	6
温度（WD）	−0.31	−0.23	−0.27	−0.25	−0.24	−0.22
PH值	−0.42	−0.42	−0.35	−0.39	−0.32	−0.39
电导率（DDL）	−0.38	−0.38	−0.38	−0.38	−0.38	−0.38
浊度（ZD）	0.48	0.43	0.44	0.43	0.43	0.43
溶解氧（RJY）	−0.29	−0.22	−0.22	−0.22	−0.22	−0.23
盐度（YD）	0.54	0.56	0.46	0.58	0.51	0.54
电压（DY）	−0.13	−0.13	−0.13	−0.13	−0.12	−0.12

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