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基于差分回归模型和可迁移长短期记忆网络集成的三沙湾水温预测

赖晓倩 余镒琦 梁中耀 陈火荣 陈能汪

赖晓倩,余镒琦,梁中耀,等. 基于差分回归模型和可迁移长短期记忆网络集成的三沙湾水温预测[J]. 海洋学报,2023,45(4):165–178 doi: 10.12284/hyxb2023027
引用本文: 赖晓倩,余镒琦,梁中耀,等. 基于差分回归模型和可迁移长短期记忆网络集成的三沙湾水温预测[J]. 海洋学报,2023,45(4):165–178 doi: 10.12284/hyxb2023027
Lai Xiaoqian,Yu Yiqi,Liang Zhongyao, et al. Water temperature prediction in the Sansha Bay based on the integration of differential regression model and transportable long short-term memory network[J]. Haiyang Xuebao,2023, 45(4):165–178 doi: 10.12284/hyxb2023027
Citation: Lai Xiaoqian,Yu Yiqi,Liang Zhongyao, et al. Water temperature prediction in the Sansha Bay based on the integration of differential regression model and transportable long short-term memory network[J]. Haiyang Xuebao,2023, 45(4):165–178 doi: 10.12284/hyxb2023027

基于差分回归模型和可迁移长短期记忆网络集成的三沙湾水温预测

doi: 10.12284/hyxb2023027
基金项目: 福建省海洋经济发展补助资金项目(ZHHY-2019-1);国家重点研发计划(2016YFC0502901)
详细信息
    作者简介:

    赖晓倩(1998-),女,福建省龙岩市人,助理工程师,主要从事大数据建模研究。E-mail:xiaoqianl1_2020@163.com

    通讯作者:

    陈能汪(1976-),男,教授,主要从事海陆界面生态环境研究。E-mail:nwchen@xmu.edu.cn

  • 中图分类号: P731.11

Water temperature prediction in the Sansha Bay based on the integration of differential regression model and transportable long short-term memory network

  • 摘要: 水温预测是保障近海渔业生产和环境安全的关键技术。现有的数值模型开发成本大,业务化应用不足。本文提出了一种集成差分回归(Differential Regression, DR)和可迁移长短期记忆网络(Transferable Long Short-Term Memory, TLSTM)的水温预测方法。以厦门湾(源域,数据多)和三沙湾(目标域,数据少)水温为研究对象,根据三沙湾在线监测水温和预报气温数据建立了DR模型,根据厦门湾长时间监测水温数据建立了TLSTM模型,采用变权算法将纯差分回归模型、混差分回归模型和TLSTM模型集成为三沙湾DR-TLSTM模型,对模型性能进行了评估,并与仅根据三沙湾少量监测数据建立的LSTM模型效果进行了对比。结果表明:(1) TLSTM模型的预测精度优于基于目标域少量数据建立的LSTM模型;(2) DR-TLSTM集成模型具有较高的预测精度,未来1~7 d预测的均方根误差为0.13~0.77℃,未来1~3 d预测的均方根误差小于0.4℃;(3) DR-TLSTM集成模型可有效预测水温骤升或骤降趋势,对水温突变点的预测均方根误差为0.29~1.09℃。基于本文建立的DR-TLSTM集成模型,实现了三沙湾渔业水域的水温预警预报业务化信息服务。
  • 图  1  基于差分回归(DR)与可迁移长短期记忆网络(TLSTM)集成的三沙湾DR-TLSTM模型建模流程

    Fig.  1  Modeling process of DR-TLSTM model of Sansha Bay based on differential regression (DR) and transferable long short-term memory (TLSTM)

    图  2  迁移学习对模型预测三沙湾未来1~7 d水温的影响

    a, c, e. 最高温100次重复测试的均方根误差(RMSE)、平均绝对误差(MAE)、拟合优度(R2)的平均值和标准差;b, d, f. 最低温100次重复测试的均方根误差(RMSE)、平均绝对误差(MAE)、拟合优度(R2)的平均值和标准差

    Fig.  2  The influence of transfer learning on the prediction of water temperature for the next 1−7 days in the Sansha Bay

    a, c, e. Means and standard deviations of root mean square error (RMSE), mean absolute error (MAE), goodness of fit (R2) of 100 replicates at the maximum temperature; b, d, f. means and standard deviations of root mean square error (RMSE), mean absolute error (MAE), goodness of fit (R2) of 100 replicates at the minimum temperature

    图  3  变权组合对模型预测三沙湾未来1~7 d水温的影响

    a, c, e. 最高温100次重复测试的均方根误差(RMSE)、平均绝对误差(MAE)、拟合优度(R2)的平均值和标准差;b, d, f. 最低温100次重复测试的均方根误差(RMSE)、平均绝对误差(MAE)、拟合优度(R2)的平均值和标准差

    Fig.  3  Influence of variable-weight combination on the predicted water temperature for the next 1− 7 days in the Sansha Bay

    a, c, e. Means and standard deviations of root mean square error (RMSE), mean absolute error (MAE), goodness of fit (R2) of 100 replicates at the maximum temperature; b, d, f. means and standard deviations of root mean square error (RMSE), mean absolute error (MAE), goodness of fit (R2) of 100 replicates at the minimum temperature

    图  4  变权组合对模型预测三沙湾未来1~7 d水温突变点的影响

    a, c, e. 最高温100次重复测试的均方根误差(RMSE)、平均绝对误差(MAE)、拟合优度(R2)的平均值和标准差;b, d, f. 最低温100次重复测试的均方根误差(RMSE)、平均绝对误差(MAE)、拟合优度(R2)的平均值和标准差

    Fig.  4  Influence of variable-weight combination on the prediction of water temperature sudden change point for the next 1−7 days in the Sansha Bay

    a, c, e. Means and standard deviations of root mean square error (RMSE), mean absolute error (MAE), goodness of fit (R2) of 100 replicates at the maximum temperature; b, d, f. means and standard deviations of root mean square error (RMSE), mean absolute error (MAE), goodness of fit (R2) of 100 replicates at the minimum temperature

    图  5  三沙湾未来1~7 d水温实测值和模型预测值对比(2021−2022年)

    a, b. 第1天对比结果;c, d. 第2天对比结果;e, f. 第3天对比结果;g, h. 第4天对比结果;i, j. 第5天对比结果;k, l. 第6天对比结果;m, n. 第7天对比结果

    Fig.  5  Comparison of observed and predicted water temperature for the next 1−7 days in the Sansha Bay (2021−2022)

    a, b. Comparison results on day 1; c, d. comparison results on day 2; e, f. comparison results on day 3; g, h. comparison results on day 4; i, j. comparison results on day 5; k, l. comparison results on day 6; m, n. comparison results on day 7

    图  6  基于差分回归(DR)与可迁移长短期记忆网络(TLSTM)集成的DR-TLSTM模型应用于三沙湾水温预警预报业务化的信息系统界面

    Fig.  6  Operational information system interface based on water temperature early warning and forecast in the Sansha Bay by DR-TLSTM model based on differential regression (DR) and transferable long short-term memory (TLSTM)

    表  1  长短期记忆网络(LSTM)模型结构和数学表达式

    Tab.  1  Structures and mathematical expressions of long short-term memory (LSTM) model

    结构数学表达式
    输入门$ {i}_{t}=\sigma \left({W}_{i}\cdot \left[{h}_{t-1},{x}_{t}\right]+{b}_{i}\right) $
    遗忘门$ {f}_{t}=\sigma \left({W}_{f}\cdot \left[{h}_{t-1},{x}_{t}\right]+{b}_{f}\right) $
    输出门$ {o}_{t}=\sigma \left({W}_{o}\cdot \left[{h}_{t-1},{x}_{t}\right]+{b}_{o}\right) $
    临时记忆单元${\widehat{C} }_{t}=\mathrm{tanh}\left({W}_{c}\cdot \left[{h}_{t-1},{x}_{t}\right]+{b}_{c}\right)$
    记忆单元$ {C}_{t}={f}_{t}\odot {C}_{t-1}+{i}_{t}\odot {\widehat{C}}_{t} $
    隐藏单元$ {h}_{t}={o}_{t}\odot \mathrm{t}\mathrm{a}\mathrm{n}\mathrm{h}\left({C}_{t}\right) $
    注:式中,$ {i}_{t} $为输入门;$ {f}_{t} $为遗忘门;$ {o}_{t} $为输出门;$ {\widehat{C}}_{t} $为t时刻临时记忆单元的输出;$ {C}_{t} $为t时刻记忆单元的输出;$ {h}_{t} $为t时刻隐藏单元的输出;$ {x}_{t} $为t时刻的输入数据;$ \sigma $为Sigmoid激活函数;$ \mathrm{t}\mathrm{a}\mathrm{n}\mathrm{h} $为双曲正切激活函数;$ {W}_{i} $、$ {W}_{f} $、$ {W}_{c} $、$ {W}_{o} $为权重矩阵;$ {b}_{i} $、$ {b}_{f} $、$ {b}_{c} $、$ {b}_{o} $为偏置向量;$ \odot $为逐元素点积运算。
    下载: 导出CSV

    表  2  长短期记忆网络(LSTM)模型主要参数和设定值

    Tab.  2  Main parameters and set values of long short-term memory (LSTM) model

    主要参数设定值
    LSTM层1神经元个数48
    正则化层1正则化系数0.2
    LSTM层2神经元个数32
    正则化层2正则化系数0.2
    损失函数MSE
    优化器Adam
    批处理大小32
    迭代轮次150
    注:MSE为均方误差,Adam为基于适应性低阶矩估计的一阶梯度优化算法结果。
    下载: 导出CSV

    表  3  三沙湾水温预警等级的划分

    Tab.  3  Classification of water temperature warning levels in the Sansha Bay

    温度T<10℃10℃≤T<12℃12℃≤T<14℃14℃≤T≤28℃28℃<T≤30℃30℃<T≤32℃T>32℃
    等级红色低温橙色低温黄色低温正常黄色高温橙色高温红色高温
    下载: 导出CSV

    表  4  迁移学习测试时100次重复实验的平均值统计

    Tab.  4  Statistics of the means of 100 replicates in transfer learning test

    预测天数评价指标目标域LSTM模型TLSTM模型(未微调)TLSTM模型
    最高温最低温最高温最低温最高温最低温
    第1天RMSE/℃5.425.690.420.310.380.28
    MAE/℃5.305.540.360.280.350.25
    R20.8230.8580.9900.9940.9910.995
    第2天RMSE/℃5.575.890.520.390.470.34
    MAE/℃5.455.720.430.330.430.30
    R20.8370.8330.9680.9860.9680.987
    第3天RMSE/℃5.746.020.620.470.570.43
    MAE/℃5.615.870.520.400.510.37
    R20.7950.8120.9310.9710.9310.971
    第4天RMSE/℃5.966.170.750.570.680.52
    MAE/℃5.846.020.610.460.610.44
    R20.7400.8050.8840.9500.8860.951
    第5天RMSE/℃6.226.370.840.640.760.59
    MAE/℃6.096.220.690.520.680.50
    R20.6710.7950.8340.9320.8390.933
    第6天RMSE/℃6.426.530.910.710.830.65
    MAE/℃6.306.380.750.580.750.56
    R20.6650.7600.7830.9130.7940.914
    第7天RMSE/℃6.616.700.970.790.880.73
    MAE/℃6.496.540.800.650.810.64
    R20.6530.7490.7370.8820.7590.884
    下载: 导出CSV

    表  5  变权组合测试时100次重复实验的平均值统计

    Tab.  5  Statistics of the means of 100 replicates in variable-weight combination test

    预测天数评价指标TLSTM模型纯差分回归模型混差分回归模型DR-TLSTM集成模型
    最高温最低温最高温最低温最高温最低温最高温最低温
    第1天RMSE/℃0.380.280.160.140.150.150.130.13
    MAE/℃0.350.250.120.110.110.110.110.11
    R20.9910.9950.9940.9960.9950.9950.9950.996
    第2天RMSE/℃0.470.340.300.250.310.250.260.22
    MAE/℃0.430.300.230.300.220.170.220.18
    R20.9680.9870.9780.9890.9780.9890.9780.989
    第3天RMSE/℃0.570.430.450.370.480.370.390.33
    MAE/℃0.510.370.340.290.330.260.330.27
    R20.9310.9710.9460.9750.9470.9750.9470.975
    第4天RMSE/℃0.680.520.600.480.630.480.510.44
    MAE/℃0.610.440.460.380.430.350.430.36
    R20.8860.9510.9020.9560.9060.9560.9050.956
    第5天RMSE/℃0.760.590.710.570.740.570.600.51
    MAE/℃0.680.500.550.470.530.440.520.43
    R20.8390.9330.8630.9390.8700.9410.8670.940
    第6天RMSE/℃0.830.650.810.670.830.670.680.59
    MAE/℃0.750.560.660.580.630.540.620.52
    R20.7940.9140.8220.9170.8340.9220.8310.921
    第7天RMSE/℃0.880.730.930.790.950.790.770.69
    MAE/℃0.810.640.770.680.740.650.710.63
    R20.7590.8840.7750.8840.7930.8890.7910.890
    下载: 导出CSV

    表  6  突变点测试时100次重复实验的平均值统计

    Tab.  6  Statistics of the means of 100 replicates in sudden change point prediction test

    预测天数评价指标TLSTM模型纯差分回归模型混差分回归模型DR-TLSTM集成模型
    最高温最低温最高温最低温最高温最低温最高温最低温
    第1天RMSE/℃0.440.290.420.320.400.340.350.29
    MAE/℃0.400.390.360.300.330.290.330.29
    R20.9920.9920.9950.9930.9960.9930.9960.994
    第2天RMSE/℃0.710.610.750.630.780.630.650.57
    MAE/℃0.660.600.580.600.560.520.570.56
    R20.9400.9350.9540.9510.9540.9510.9540.951
    第3天RMSE/℃1.070.771.020.671.080.670.890.61
    MAE/℃0.950.720.800.750.760.690.760.71
    R20.7980.7050.8060.7470.8080.7470.8070.747
    第4天RMSE/℃1.310.951.010.811.060.810.870.74
    MAE/℃1.130.950.850.920.800.850.800.87
    R20.7290.6930.7820.7800.7850.7810.7840.781
    第5天RMSE/℃1.320.951.180.991.230.991.010.90
    MAE/℃1.040.940.890.950.850.880.840.88
    R20.6660.6720.7880.7290.7940.7310.7920.730
    第6天RMSE/℃1.331.001.191.071.231.071.010.95
    MAE/℃1.031.051.001.070.941.000.940.98
    R20.6930.6910.7120.6940.7220.6980.7200.698
    第7天RMSE/℃1.231.031.311.111.341.121.090.98
    MAE/℃1.071.061.021.020.970.970.950.95
    R20.6300.4500.6360.5260.6510.5290.6490.530
    下载: 导出CSV

    表  7  基于差分回归(DR)与可迁移长短期记忆网络(TLSTM)集成的DR-TLSTM模型对三沙湾水温预警准确率的统计结果

    Tab.  7  Statistical results of DR-TLSTM model based on differential regression (DR) and transferable long short-term memory (TLSTM) for water temperature early warning accuracy in the Sansha Bay

    设备编号第1天第2天第3天第4天第5天第6天第7天
    1#渔排基96.27%94.03%91.04%88.06%84.33%82.84%82.09%
    2#渔排基95.52%94.03%91.04%88.06%85.82%83.58%80.60%
    3#渔排基97.76%95.52%91.79%91.79%88.06%87.31%79.10%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-06
  • 修回日期:  2022-09-26
  • 网络出版日期:  2023-04-10
  • 刊出日期:  2023-03-31

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