Water Research 211 (2022) 118040
Contents lists available at ScienceDirect
Water Research
journal homepage: www.elsevier.com/locate/watres
Long-term prediction of sea surface chlorophyll-a concentration based on
the combination of spatio-temporal features
Liu Na a, Chen Shaoyang a, *, Cheng Zhenyan b, Wang Xing a, Xiao Yun c, Xiao Li a, Gong Yanwei a,
Wang Tingting a, Zhang Xuefeng a, Liu Siqi a
a
b
c
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
College of Fisheries, Tianjin Agricultural University, Tianjin 300384, China
Xian Research Institute of Surveying and Mapping, Xian 710061, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
HABs
Chl-a
CNN-LSTM model
Long-term rolling prediction
Time series analysis
Harmful algal blooms (HABs) events have a serious impact on marine fisheries and marine management. They
occur globally with high frequency and are characterized by a long duration and difficult governance. HABs
incidents have occurred in the South China Sea (SCS), and the frequency of occurrence has been on the rise in
recent decades. Predicting the long-term chlorophyll-a (Chl-a) concentration has the potential to facilitate longterm monitoring and early warning of HABs events. Currently, long-term predictions of ocean circulation and
temperature are common, while long-term predictions of marine biochemistry are still in their infancy. Tradi­
tional Chl-a prediction methods have problems, such as low accuracy and the inability to carry out long-term
predictions. This research improved the CNN-LSTM model by combining spatio-temporal features to predict
Chl-a concentrations. This model can extract both the temporal and spatial features of Chl-a, expand the dataset,
and improve the prediction accuracy and training speed. The predictions were made using a Chl-a dataset for the
Reed Tablemount in the SCS. The time series of Chl-a used was the satellite data of NASA’s official website from
January 2002 to June 2020. The results indicate that the predictions of the CNN-LSTM model are better than
those of the LSTM and SARIMA models. The five-year long-term rolling prediction of Chl-a was carried out, and
the three-year Pearson correlation coefficient reached 0.5. The novelty of this study is the realization of a threeyear long-term prediction of Chl-a concentrations. The Mann-Kendall trend test method and the least square
method were used to fit the straight line to detect the trend of the five-year predicted value and the true value,
respectively. The results indicated that the prediction value and true value of the sea surface Chl-a from 2015 to
2020 both exhibited an overall upward trend. In addition, the prediction performance of the model in large-scale
prediction is better than that in small-scale prediction.
1. Introduction
According to the Harmful Algae (Red Tide) Event Database (HAE­
DAT), in the 30 years after 1985, there were more than 8000 harmful
algal blooms (HABs) events worldwide, and the frequency of occur­
rences has gradually increased. The South China Sea (SCS) has a high
quality of seawater due to factors such as deep water and the distance
from land. However, HABs frequently occur in the SCS. Studies have
determined that between 1980 and 2003, there were more than 700
HABs occurrences in the SCS, with 170 in the 1980s and 440 in the
1990s (Wang et al., 2008). HABs in the SCS have exhibited an upward
trend in recent decades (Zhang, 2013). HABs are one of the three major
marine disasters worldwide. After an outbreak, it has the characteristics
of a long duration and difficult governance. When HABs occur, they
negatively affect fisheries (McOwen et al., 2015) and marine ecosys­
tems, causing substantial property loss and even threatening human life
and health. Therefore, it is essential to build a HABs prediction model
and a marine water quality prediction system (Rostam et al., 2021). In
the past, the unpredictability of red tide fluctuations caused difficulty in
handling HABs events. The red tide monitoring and early warning has
ushered in the "big data era", making long-term and large-scale red tide
monitoring and early warning possible.
Park et al. (2019) summarized previous studies (Seferian et al., 2014)
and demonstrated that biogeochemical factors, such as acidity, oxygen,
* Corresponding author.
E-mail address: nmdiscsy@126.com (C. Shaoyang).
https://doi.org/10.1016/j.watres.2022.118040
Received 29 September 2021; Received in revised form 9 December 2021; Accepted 2 January 2022
Available online 4 January 2022
0043-1354/© 2022 Elsevier Ltd. All rights reserved.
L. Na et al.
Water Research 211 (2022) 118040
fishery production (Chavez et al., 2003; Chassot et al., 2010; Stock et al.,
2017). Moreover, it can provide valuable support for scientific sea area
management and red tide control. Currently, long-term predictions of
ocean circulation and temperature are common, while long-term pre­
dictions of marine biochemistry are still in their infancy. Existing Chl-a
prediction studies have been mostly short-term predictions (Zhao et al.,
2017), and most of the prediction targets have been lakes (Hou et al.,
2004; Zeng et al., 2006; Chen et al., 2014a; Li et al., 2017c; Barzegar
et al., 2020, 2021) and rivers (Lee and Lee, 2018), with a lack of
long-term predictions for the multi-year Chl-a concentrations in sea
areas. The prediction algorithm that is suitable for one water source may
not be suitable for other water sources because they have different
characteristics in terms of hydrology, climate, geochemistry, and bio­
logical characteristics (Rostam et al., 2021). It is necessary to model the
medium- and long-term predictions of Chl-a, concentrations but
long-term predictions are exceptionally difficult because long-term
predictions require more historical data input (Li et al., 2017b). The
robustness of LSTMs in medium-and long-term simulations (Wang et al.,
2019) provides support for long-term predictions. Some scholars (Chen
et al., 2020; Yu et al., 2020) have predicted the long-term trends in Chl-a
concentrations, but they have not compared these results with the true
values to verify the accuracy predictions. The National Aeronautics and
Space Administration (NASA) Global Modeling and Assimilation Office
(GMAO) Subseasonal to Seasonal Forecast System (GEOS-S2S) estab­
lished a long-term global biogeochemical prediction model, conducted a
9-month long-term seasonal prediction of the global ocean Chl-a con­
centrations (Rousseaux and Gregg, 2017; Rousseaux et al., 2021), and
have continued to improve the model. In addition, Park et al. (2019)
attempted a longer time span for Chl-a predictions using the Global
Earth System Model (ESM) (Stock et al., 2014) to predict regional sea­
sonal to multi-year ocean Chl-a fluctuations, including predicting the
fluctuations in the Somali Sea up to 2–3 years in advance, but the pre­
diction time for the tropical Pacific was only 12 months. Seferian et al.
(2014) used ESM in 2014 to verify that the effective predictable range of
tropical Pacific net primary productivity (NPP) could be extended to
three years. Studies have demonstrated that the NPP is the most sensi­
tive to the surface Chl-a concentration (Lee et al., 2015) and is usually
modeled as a function of Chl-a (Westberry et al., 2008). It is inferred
from this that the prediction time of Chl-a concentration in the tropical
Pacific could also be extended to interannual scales.
The current study has improved the CNN-LSTM model. Compared
with the traditional LSTM time-series prediction model, this model adds
the process of extracting spatial data features that are highly correlated
with the target prediction area, supplements the dataset, and effectively
ameliorates the poor prediction performance of traditional LSTMs for
small data volumes. The model was tested and compared with LSTM and
SARIMA models. Then, the three-dimensional spatio-temporal charac­
teristics of the Chl-a sequence were extracted and analyzed, with a longterm rolling prediction of Chl-a concentrations over a five-year time
span. Long-term Chl-a predictions help to understand Chl-a fluctuations
and provides services for the establishment of a red tide early warning
research system.
and primary production, may be more predictable than their physical
counterparts. Therefore, the concentration of chlorophyll-a (Chl-a), a
factor that is commonly used for oceans, represents the concentration of
algae (Yang et al., 2019) as a key indicator for monitoring the degree of
seawater nutrition (Zou et al., 2020). The over-proliferation of marine
algae triggers red tides, and predicting the temporal and spatial changes
in Chl-a concentrations can provide timely insights into the algae and
marine ecological conditions to provide an early warning of red tide
disasters (Xu et al., 2019). The original Chl-a prediction model is a
simple first-order equation proposed by Vollenweider (1975). This
model does not consider other factors affecting Chl-a. There are many
factors that affect the concentration of Chl-a, such as sea surface tem­
perature, wind speed, light transmittance of the sea water, and whether
it is near the shore (Chen et al., 2011; Carneiro et al., 2014). Subse­
quently, scholars established a theoretical analysis model to predict
Chl-a concentration based on the nature of the water body (Jørgensen
et al., 1978; Wu et al., 2018). This type of model can consider the in­
teractions between various elements in the water body, but the diversity
of water quality variables in the ocean may make correct modeling or
parameterization difficult (Dutkiewicz et al., 2020). The autoregressive
integrated moving average model (ARIMA) is the most common type of
predictive model for time-series data. The ARIMA model has a simple
structure but requires that the time-series data must be stable. The
seasonal difference autoregressive moving average model (SARIMA) is
an advanced ARIMA model that can support the seasonal variables of the
time series. In the past, Chl-a determination primarily relied on-site
sampling, which has the disadvantages of high cost and slow speed
(Wu and Liu, 2012). With the development of satellite remote sensing
technology, remote sensing monitoring of Chl-a (Gitelson et al., 2007),
and remote sensing inversion (O’Reilly et al., 1998; Dall’Olmo et al.,
2003), research has become increasingly mature. With the diversifica­
tion of the means of obtaining information, a large amount of variable
water quality data can be obtained. Therefore, machine learning models
are widely used in water quality variable predictions (Ark et al., 2015;
Xiao et al., 2017), such as artificial neural networks (Lu et al., 2008;
Vilas et al., 2011; Alizadeh and Kavianpour, 2015; Sinshaw et al., 2019;
Shi et al., 2020), the random forest method (Li et al., 2017a, 2018;
Yajima and Derot, 2018), and support vector machines (Noori et al.,
2015; Park et al., 2015; Xu et al., 2015; Kisi and Parmar, 2016; Fijani
et al., 2019). In water quality predictions, machine learning focuses
more on prediction accuracy than on model structures (Elith and
Leathwick, 2009). Deep learning (DL) is a special type of machine
learning (Guo et al., 2020) that has been widely used in the field of data
prediction in recent years, and its predictions using large datasets are
better than with machine learning. DL prediction models have been
widely used in the field of marine environment in the past two years,
such as ship navigation (Liu et al., 2020), waves (Bao et al., 2020), sea
height anomalies (SSHA) (Song et al., 2020), and sea surface tempera­
tures (Xie et al., 2020), as well as a few studies on predicting ocean Chl-a
concentrations (Rostam et al., 2021). Most current studies have used
independent DL models for short-term Chl-a predictions. Researchers
have mostly used the LSTM model to predict Chl-a concentrations (Cho
et al., 2018; Zheng et al., 2021), with some using the convolutional
neural network (CNN) model (Choi et al., 2019). Yussof et al. (2021)
implemented the LSTM and CNN methods to predict HABs events on the
west coast of Sabah eight days in advance and determined that the
predictions of the LSTM model are better than those of the CNN model.
Because the LSTM method can learn the long-term dependence, which
the CNN method cannot, the LSTM method can memorize the
time-series information. However, the correlation coefficient of the
LSTM model is still very low. Barzegar et al. (2020) used the hybrid
CNN-LSTM model for the first time to perform short-term Chl-a pre­
dictions on Small Prespa Lake in Greece and verified that the hybrid
CNN-LSTM model is better than the DL independent model in predicting
Chl-a levels.
Chl-a seasonal and multi-year predictions are necessary to maintain
2. Materials and methods
2.1. Data set
The study area was located in the SCS. The SCS is a vital and
controversial part of global ocean governance. While its abundant re­
sources provide opportunities for the surrounding countries, it also
brings challenges to the environmental protection of the SCS. Sur­
rounding countries have a common responsibility for the environment in
the SCS. However, the controversy surrounding the SCS complicates its
environmental protection, and there is no complete governance system.
The balance of the SCS and the management of eutrophication of the SCS
require long-term environmental cooperation between China and the
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Water Research 211 (2022) 118040
neighboring countries. Water pollution in the SCS predominantly in­
cludes land transportation pollution, heavy metal pollution of coral
reefs, and oil and gas development pollution. The selected dataset covers
the global oceans and SCS from 11.1◦ N–12.1◦ N, 115.8◦ E–117.5◦ E, and
covers part of the Reed Tablemount in the SCS (Fig. 1). The Reed
Tablemount is rich in oil and gas resources. Drilling mud discharge,
domestic sewage discharge, and pipeline-laying sediments in oil and gas
exploration and development activities may cause pollution to the sea.
Therefore, the environmental conditions of the sea near the Reed
Tablemount should be considered.
Using a single variable input has a higher accuracy than using mul­
tiple variable inputs (Xiao et al., 2017; Yussof et al., 2021). In this study,
the Chl-a concentration was selected as the only variable input. The SCS
is far from the mainland, and it is difficult to cover a wide range of sea
areas with actual measurements and station observations. Therefore,
remote sensing was used to obtain the long-term and large-scale
observation data. The data used in the experiment were obtained from
the official website of NASA Ocean Color (https://oceancolor.gsfc.nasa.
gov/). The downloaded data were the vector data of the satellite time
series. Because of the large number of missing values in the daily Chl-a
data, we selected the second-level Chl-a semi-monthly synthetic product
data of the modified sensor from January 2002 to June 2020. The data
comes from Terra and Aqua satellites, and the accuracy of the remote
sensing data was 1000 m. NASA’s research on the inversion of Chl-a
concentration has reached a relatively mature stage. O’Reilly et al.
(1998) obtained a model ocean chlorophyll x (OCx) for global Chl-a
Fig. 1. The location of the study area, the area covered by the grids is the research area.
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Water Research 211 (2022) 118040
concentration inversion that is suitable for various types of remote
sensing data through experimental summaries. The global Chl-a inver­
sion algorithm is not as accurate as the regional inversion algorithm, but
because the study area is Case 1 water, the inversion is relatively reliable
(O’Reilly et al., 1998).
The downloaded Chl-a remote sensing data had a resolution of 1 km
× 1 km. First, we re-analyzed the data, divided it into grids, and
expanded it 20 times to produce a grid product with a spatial resolution
of 20 km × 20 km (Fig. 1). The value of a single grid was replaced by the
average of 400 original data points. The SCS is a sea area with sub­
stantial cloud coverage, especially in summer when the cloud coverage
can reach 80%, which could cause missing and abnormal sensor data.
Therefore, to observe the continuity of the data, it was necessary to
remove the outliers outside the interquartile range when studying the
temporal and spatial changes in the Chl-a concentration, and spline
interpolation was used for the missing values. Before entering the model,
the original data were normalized with min–max normalization to
prevent gradient changes and improve the convergence speed of the
model. The normalization and min–max scaling techniques scale be­
tween 0 and 1, executed in the scikit-learn preprocessing library.
For each grid, the dataset was divided into a training set, validation
set, and prediction set according to the time periods. In the time
dimension, the semi-monthly Chl-a data from January 2002 to June
2020 included 444 data, with 210 data from January 2002 to September
2010 as the training set, 114 data from September 2010 to June 2015 as
the validation set, and 120 data from June 2015 to June 2020 as the
prediction set.
information is stored and remember the long-term information of the
time series for future predictions. When the data flow in the network, it
can be stored, removed, and added according to whether the informa­
tion is needed, effectively coping with the problem of vanishing gradi­
ents (Shi et al., 2015). Therefore, LSTM can predict longer sequences
and widely spaced sequences (Gers et al., 2002). LSTM has advantages in
time series modeling, has strong learning and generalization abilities,
and has a good predictive effect on nonstationary data.
However, LSTM neural networks are complex, and there are many
processing parameters during the training process. In addition, LSTM
training is slow, computationally intensive, time consuming, and re­
quires advanced hardware systems. For RNN gradient disappearance
and gradient explosion problems, there is a certain degree of improve­
ment in LSTM, but this has not been completely solved. LSTM can only
extract relatively long time-series information (Weninger et al., 2015),
and the predictions for longer time series and long-term trends worsen.
2.2.3. CNN-LSTM
CNN neurons can respond to the surrounding units when processing
data and are generally used to process images. CNNs include an input
layer, convolutional layer, activation layer, pooling layer, and fully
connected layer. The CNN-LSTM model in this study combines the CNN
and LSTM. It has the advantage of CNN to extract multi-dimensional
image features and can perceive spatial information features. Howev­
er, it retains the advantage of LSTM in processing the time series. In the
CNN-LSTM model, the CNN extracts the deep features of the index. The
LSTM model is then applied to perform predictions using these deep
features (Huang et al., 2018). CNN-LSTM contains six modules: an input
layer, convolution layer, pooling layer, dropout layer, LSTM layer, and
output layer. Compared with the LSTM time series model, the con­
volutional layer, pooling layer, and dropout layer of the CNN are added.
The CNN-LSTM automatically generates a feature extractor during the
training process, and the convolution kernel performs a
multi-dimensional feature extraction on the input data for local
perception and integrates all the information through shared weights.
Nonlinear mapping is performed on the convolution output through the
activation function. Commonly used activation functions are the sig­
moid function, Tanh function, and ReLU. The convolutional layer can
transfer the shape information to the next layer in the form of the same
dimension. The data circulating in the convolutional layer are shape
data, that is, multidimensional data. The pooling layer can reduce the
dimensionality into one dimension, lose spatial information, and then
pass it to the LSTM layer. The pooling process includes max pooling and
average
pooling.
The
pooling
layer
primarily
performs
dimensionality-reduction operations on the features, reduces the num­
ber of data and parameters, and reduces overfitting.
2.2. Chl-a prediction models
2.2.1. SARIMA
Box et al. (1976) proposed the traditional ARIMA prediction model,
which is more suitable for datasets with stable sequence trends, that is,
stationary data sets. Therefore, in the prediction process, we first elim­
inated the trend and seasonal influence and converted the data into
stable data. SARIMA (p,d,q)(P,D,Q)s have seven parameters, adding
seasonal parameters to ARIMA (p,d,q).
The structure of the SARIMA model is as follows (Ma et al., 2021):
⎧
( S( d D )
( S) )
⎪
⎪
⎨ Φ(L)Ap L ∇ ∇S xi = Δq (L)Bq L εt
2
(1)
E(εt ) = 0, Var(εt ) = σ S , E(εt |εS ) = 0, S ∕
=t
⎪
⎪
⎩ E(xS εt ) = 0, S < t
In the formula, L is the delay operator, Ap (LS ) is the p-order autore­
gressive operator, Bq (LS ) is the q-order seasonal moving average oper­
ator, ∇d = (1 − L)d is the difference operation, and ∇DS = (1 − LS )d is
seasonal difference operation.
First, we determined whether the data were stationary data using the
stationarity test. Then, we determined whether the model satisfied the
residual white noise test. Finally, the autocorrelation graph (acf) and
partial autocorrelation graph (pacf) were used to determine the model
parameters.
2.3. Implementation of dl prediction models
2.3.1. Simulation environment
The experiment was carried out on a PC with the following features:
Hardware: Processor Intel(R) Core(TM) i7–7500 U CPU, 8 GB
memory, dual graphics card AMD Radeon(TM) 530, and Intel(R) HD
Graphics 620.
Software: Windows64-bit operating system, PyCharm-Professional2020.2.3 compiler, Anaconda3–2.4.1-Windows-x86_64 environment
configuration, Python3.5.6, Keras2.2.2, and TensorFlow1.10.0.
2.2.2. LSTM
The LSTM network was originally a recurrent neural network (RNN)
proposed by Hochreiter and Schmidhuber (Hochreiter and Schmid­
huber, 1997). The neurons of the traditional RNN contain self-feedback
connections, and the output is jointly determined by the input and the
previous output, making it capable of remembering. However, as the
time interval increases, the information is multiplied by the decimal
multiple times during the neuron flow process and is lost, resulting in the
disappearance of the gradient (Zeng and Zhang, 2013). The influence of
the current output on the subsequent output weakens until it disappears.
Therefore, useful information cannot be continuously remembered. The
LSTM can continuously circulate information to ensure that the
2.3.2. LSTM
When using the LSTM model to predict the Chl-a concentration, the
hidden layer length is 50, the time step is 100, the learning rate is 1e-7,
and the batch_size is set to 16. LSTM implements Adam optimization and
80 rounds of sample training. The model predicts one time step at a time,
and when performing multi-step prediction, the previous prediction
output is added and iterated to the next sequence for the next prediction
process.
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Water Research 211 (2022) 118040
2.3.3. CNN-LSTM
2.3.3.3. CNN-LSTM prediction implementation. The sea surface is
divided into grids in space, and the Chl-a data are condensed into one
observation value at each grid point. The Chl-a value of a single grid
point is correlated with the adjacent grid points, and the related grid
data constitute the spatial data. The Chl-a value at each grid point is a
time series, and the Chl-a value has a correlation (dependency) with the
historical data, which constitutes the time dimension data. The time
dimension data and spatial data constitute the 3D time-series grid data.
In the CNN-LSTM prediction process, we selected the historical 3D timeseries grid data of the Chl-a concentration to predict the future 3D timeseries grid data and scroll forward through the window to make the next
prediction. Taking the D42 grid as an example, the D42 grid needs to be
tested for mutual information before the prediction. It was found that
the D43 and E42 grids adjacent to the D42 grid had a strong correlation
with the D42 grid (greater than 0.7). Therefore, this study input the
three-dimensional Chl-a time-series data of the three grid areas of D42,
D43, and E42. In the single-step prediction process, 30 time steps of the
sample are used to predict the next Chl-a data of the target grid. Sliding
forward one step at a time predicts the next data point in the target grid
area. In the rolling prediction process, the predicted data are added to
the sample, and the following 72 time steps (three years) of the Chl-a
concentration data are used to predict the subsequent Chl-a concentra­
tion data. As the number of iterations increases, the prediction accuracy
gradually decreases (Fig. 3).
2.3.3.1. Mutual information. When using the CNN-LSTM model to pre­
dict the Chl-a concentration, a grid mutual information analysis is per­
formed first. Mutual information is a measure of the statistical
correlation between two random variables that can measure the corre­
lation between two events. For two discrete variables X and Y, the
calculation is defined as follows:
(
)
∑∑
p(x, y)
I(X; Y) =
p(x, y)log
dxdy
(2)
p(x)p(y)
y∈Y x∈X
where I(X; Y)is the mutual information entropy; p(x, y) is the joint
probability distribution function of the discrete variables X and Y, and
p(x)p(y) are the marginal probability distribution functions of the
discrete variables X and Y, respectively.
If I(X; Y)=0, the variables X and Y are independent of each other. If
I(X; Y)=1, the variables X and Y are completely correlated.
2.3.3.2. CNN-LSTM training implementation. By adding a convolutional
layer, we extract the overall features of the surrounding grid area data
that are highly related to the current grid area. The model uses the
convolutional layer (Conv1D) of the unified kernel initializer. The size of
the added convolution kernel is 64, and the sigmoid activation function
is used. There are no learning parameters in the pooling process. Before
the data enters the LSTM layer, if the number of neurons is too large, it
may lead to over-learning and overfitting. Therefore, this study adds a
dropout layer and randomly removes some neurons to reduce over­
fitting, with a dropout rate of 0.001. The LSTM layer selects the tanh
activation function and 64 hidden neural units. In the LSTM layer, the
kernel layer sets the glorot_uniform initialization value, the weight
initialization uses the Zeros method, and the activation function of the
loop step is hard_sigmoid. The learning rate of the CNN-LSTM model is
set to 0.001, the batchsize is set to 8, and the training epoch is set to 150.
During the runtime, the model uses the loss function of the MSE and
performs Adam optimization (Fig. 2).
2.4. Inspection index
2.4.1. Prediction accuracy test
The root mean square error (MSE) and mean absolute error (MAE)
were calculated to test the Chl-a prediction level. The smaller the MSE
and MAE, the closer the predicted value is to the true value. A value of
0 indicates the highest accuracy. The Pearson correlation coefficient (r)
was calculated to test the rolling prediction level of the long-term Chl-a.
The r metric represents the level of linear regression between the
observed and predicted values. The larger the Pearson correlation co­
efficient, the higher the correlation between the predicted and original
Fig. 2. Program running structure diagram.
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Water Research 211 (2022) 118040
Fig. 3. The prediction process for Chl-a in D42 grid area (a) single-step prediction process and (b) rolling prediction process.
values. When r is equal to 1, the strongest correlation is observed.
n ∑
i− 1
∑
N
1 ∑
MSE =
(yi − fi )2
N i=1
(3)
N
1 ∑
MAE =
|(yi − fi )|
N i=1
(4)
∑N
− y)(fi − f )
1
2 ∑N
2 ]2
i=1 (yi − y)
i=1 (fi − f )
r = [∑
N
i=1 (yi
S=
i=2
)
(
sign xi − xj
(6)
j=1
In the equation, the value of sign(x) is as follows:
⎧
⎨1 x > 0
sign(x) = 0 x = 0
⎩
− 1x<0
(7)
S is normal distribution, E(S) = 0, Var(S) = n(n − 1)(2n + 5)/18.
Define statistics:
/√̅̅̅̅̅̅̅̅̅̅̅̅̅̅
⎧
Var(S) S > 0
⎨ (S − 1)
Z=
(8)
0S=/
0 √̅̅̅̅̅̅̅̅̅̅̅̅̅̅
⎩
(S + 1)
Var(S) S < 0
(5)
where N is the size of the prediction sample, yi is the predicted value, fi is
the true value, y is the average value of the predicted sequence, and f is
the average value of the actual sequence.
In the equations, E(Sk ) is the mean of Sk , and Var(Sk ) is the variance
of Sk .
If Z > 0, the X series shows an upward trend, and if Z < 0, the X series
shows a downward trend. The significance level α is given, and Z1− α/2
can be obtained from the normal distribution table. If |Z| > |Z1− α/2 |,
2.4.2. Trend test
2.4.2.1. Mann-Kendall test. The Mann-Kendall method (M-K method) is
a non-parametric statistical test method.
First, for the time series X{x1, x2, ⋯xn}, we define the test statistic S:
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Water Research 211 (2022) 118040
then the sequence exhibits a significant trend change at the significance
level α.
(
)
(9)
P = 2 × 1 − ∂|Z|
(D42-D43-E42-E41) combination to predict the Chl-a concentration of
the D42 grid area, compared the prediction effects of the two combi­
nations, and selected the optimal combination. We conducted mutual
information entropy experiments on all the grids (Fig. 5). Each grid area
has a strong correlation with its surrounding area. When predicting the
target grid, we selected different combinations of the target grid and its
surrounding grid areas for the comparison experiments and selected the
combination method with the best prediction.
where P is the probability of the statistical trend, and ∂|Z| can be obtained
from the normal distribution table. When P < 1− α, the trend change was
not significant; when P > 1− α, the trend changed significantly.
2.4.2.2. Least squares method. The least-squares method fits a straight
line to determine the change trend of the sequence.
y = kx + b
3.1.2. Parameter analysis
The timestep is the key parameter of the model. When predicting a
sequence, we usually need to determine the optimal timestep, that is,
how many historical values are used to predict future values. MAE (mg/
m3) and MSE (mg/m3) represent the prediction errors. For the prediction
of the Chl-a concentration in the D42 area of the target grid, we chose
two combinations (D42-D43-E42) and (D42-D43-E42-E41), set the
different timestep values, respectively, compared the sizes of MAE and
MSE, and selected the best combination method and best timestep value
(Table 1).
When predicting the Chl-a concentration in the D42 grid area, the
(D42-D43-E42) combination method was selected to have the smallest
prediction errors and best model performance (Table 1). The overall
error of the (D42-D43-E42-E41) combination method is higher than that
of the (D42-D43-E42) combination, and the prediction was poor. The
reason for this result may be that the correlation between the E41 and
D42 grids is weak, and the mutual information entropy is less than 0.6.
Both the MAE and MSE values, as well as the error, were the smallest
when the timestep value was set to 30. When the timestep value was set
to 70, the MAE and MSE errors were relatively small but slightly larger
than those with the timestep of 30, and the prediction was better. The
experiments demonstrated that within the timestep range of 100, with
the increase in the timestep, the MAE and MSE values exhibited periodic
changes. When the timestep was 30 or 70, the model provided the best
prediction when compared with the historical Chl-a concentrations in
the SCS. This may be because the Chl-a concentration presents seasonal
and periodic changes.
(10)
The least-squares method determines the trend change of the
sequence using the first-order coefficient k of the straight line. If k > 0,
the sequence exhibits an upward trend. If k = 0, then the sequence is a
stationary sequence. When k < 0, the sequence exhibits a downward
trend.
3. Results and discussion
3.1. Index analysis
3.1.1. Mutual information
Before testing the CNN-LSTM model, we first clarified the correlation
of the grid area, selected multiple grids with a strong correlation with
the target grid, extracted features, and predicted the Chl-a concentration
of the target area. The mutual information entropy was calculated to
express the correlation of the grid area. The D42 grid area is used as an
example to calculate the mutual information entropy of the D42 grid and
other grids.
The grid area of D42 has the highest correlation with D43 and E42,
and the mutual information entropy is 0.705 and 0.772, respectively,
which are both greater than 0.7 (Fig. 4). D42 has a high correlation with
the E41 grid, and the mutual information entropy is approximately 0.6.
For the prediction, we selected the (D42-D43-E42) combination and the
Fig. 4. Mutual information entropy of D42 grid.
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Water Research 211 (2022) 118040
Fig. 5. Regional correlation: (a) Mutual information entropy between all grids and (b) Masking result of mutual information entropy less than 0.7.
Table 1
MAE and MSE values of different network combinations and different timestep tests.
Timestep
MAE (mg/m3)
MSE (mg/m3)
D42-D43-E42
D42-D43-E42-E41
D42-D43-E42
D42-D43-E42-E41
10
20
30
40
50
60
70
80
90
100
6.91e-3
1.10e-2
1.09e-4
2.78e-4
2.26e-2
9.88e-3
7.28e-4
1.97e-4
6.51e-3
9.46e-3
7.30e-5
1.71e-4
8.39e-3
1.09e-2
1.19e-4
2.15e-4
2.32e-2
9.83e-3
8.23e-4
1.69e-4
9.30e-3
8.86e-3
1.38e-4
1.43e-4
8.01e-3
1.67e-2
1.08e-4
4.06e-4
2.15e-2
1.60e-2
7.38e-4
4.07e-4
1.22e-2
1.27e-2
2.69e-4
2.93e-4
160e-2
155e-2
4.10e-4
4.17e-4
By comparing the MAE and MSE errors, we determined the best value
of the network combination method and timestep. For the prediction of
the D42 grid area sequence, when selecting the combination (D42-D43E42) and set the time step to 30 or 70, the error was small, and the model
performance was good.
The epoch represents the number of training rounds for a training
sample. The timesteps were selected as 30 and 70, respectively, and
different epochs were selected based on experience to test the best
Table 2
MAE and MSE values of different timesteps and different Epochs.
Timestep
MAE (mg/m3)
MSE (mg/m3)
30
70
30
70
10
50
100
150
200
500
800
1000
2.45e-2
2.32e-2
8.53e-4
7.65e-4
7.27e-3
9.62e-3
1.00e-4
1.74e-4
6.51e-3
8.00e-3
7.30e-5
1.08e-4
6.29e-3
6.64e-3
6.80e-5
7.10e-5
5.56e-3
7.25e-3
6.10e-5
8.70e-5
5.13e-3
7.05e-3
5.60e-5
8.50e-5
576e-3
6.24e-3
6.90e-5
6.90e-5
6.36e-3
7.36e-3
8.20e-5
9.10e-5
8
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Water Research 211 (2022) 118040
performance of the model (Table 2).
When the timesteps were 30 and 70, the errors of MAE and MSE were
not significantly different. When epoch was less than 100, the error was
large, indicating that the model was not fully trained. When the epoch
was greater than 500, the values of MAE and MSE began to increase,
indicating that the model was over-fitting. Increasing the epoch will also
lead to a long training time and high cost. Through experiments, we
found that after training approximately 150 times, the model stabilized.
Therefore, we set the epoch as 150 to provide the best model (Table 2).
Through the parameter analysis, we selected the (D42-D43-E42)
combination, set the timestep to 30 and epoch to 150, and as with the
prediction model of the D42 sequence, predicted the target grid D42
sequence and calculated the predicted loss curve (Fig. 6). After 150
rounds of training, the loss of the training set and the loss of the test set
were both below 0.005.
All the grid areas were predicted, and the true and predicted values
of the Chl-a concentration in June 2015 were selected for display
(Fig. 7). The CNN-LSTM model has high accuracy in predicting the Chl-a
in the study area, the error between the predicted value and the true
value was small, and the Chl-a concentration change trend in the entire
area was consistent. This shows that our model performs well in terms of
the regional Chl-a predictions. The same model with the same parame­
ters could be applied to the Chl-a prediction of the entire region, with
strong spatial stability and universal applicability. After training on all
the grid regions, the average MAE of the study area prediction was
1.20e-2 mg/m3, and the average MSE was 2.99e-4 mg/m3.
The grid resolution was 20 km × 20 km, and the standard deviation
and coefficient of variation for calculating a single grid area were small.
The average standard deviation was 0.0158, and the average coefficient
of variation was 0.201. This demonstrates that a remote sensing reso­
lution of 1 km and a grid resolution of 20 km × 20 km can accurately
analyze the characteristics of temporal and spatial changes in Chl-a
concentrations. The Chl-a content in the study area gradually
increased from west to east and from north to south (Fig. 7). The reason
may be due to the existence of Xiongnan Jiao in the north of the study
area and the Nares Bank in the west. The Chl-a concentration near the
coral reef beach is lower than that of the open ocean, which is consistent
with the results of Yahel et al. (1998). Yahel et al. (1998) believed that
the abundance and Chl-a concentration of phytoplankton near coral
reefs are 15–65% lower than that of the adjacent open waters, and the
decline in Chl-a near coral reefs is usually related to an increase in the
degradation products.
3.2. Comparison of three models
In this study, three models were used to predict the grid area dataset:
SARIMA, LSTM, and CNN-LSTM. We used the D42 area as an example to
make the predictions, compared the prediction performance of the three
models, and calculated the 95% confidence interval of the predicted
value.
For the SARIMA model, the stationarity test was first performed on
the time series, and the P value was 6.30e-10, which is less than the
significance level of 0.05, indicating that the time series was a stationary
series, and there was no need to carry out the difference operation, that
is, d = 0. The white noise test result was p = 3.40e-28, which is less than
the significance level of 0.05. We use the autocorrelation graph (acf) and
partial autocorrelation graph (pacf) to determine p = 4 and q = 4. The
concentration of Chl-a in the SCS exhibits seasonal changes with a halfyear cycle, so s = 12 was used.
3.2.1. Single-step prediction
Three models were used to predict the D42 sequence and determine
the confidence interval (Fig. 8).
The predicted value of the CNN-LSTM model best fit the true value
curve, and the confidence interval was the narrowest, indicating that the
predicted value was the closest to the true value. The LSTM model was
second, and the SARIMA model provided the worst prediction (Fig. 8). It
can be seen from the figure that the prediction interval is included in the
true value interval, which may be that the predictive data is not sensitive
to extreme values and human influences, such as government
Fig. 6. The loss curve of Chl-a prediction in the D42 grid area.
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L. Na et al.
Water Research 211 (2022) 118040
Fig. 7. Comparison of Kriging interpolation results between true and predicted values in the study area: (a) True Chl-a value in June 2015 and (b) Predictive Chl-a
value in June 2015.
management (Yu et al., 2020).
Affected by complex geophysical and chemical effects, Chl-a changes
in the SCS have complex and multiscale characteristics. The Reed
Tablemount is located in the Nansha area of the central basin of the SCS.
The Chl-a concentration is generally low, maintained at approximately
0.1 mg/m3. The seasonal Chl-a time series of the Reed Tablemount
waters has a bimodal structure annually, with a time scale and period of
approximately 6 months. The Chl-a concentration is the highest in
winter, approximately 0.15 mg/m3, and the lowest in summer
(approximately 0.075 mg/m3). This is primarily because the SCS
monsoon is the key influencing factor for Chl-a changes in Liletan waters
(Yu et al., 2020). The strong monsoon and complex topography cause
the SCS to have a significant seasonal circulation system. In winter, the
northeast monsoon affects the sea, and the circulation in the SCS has a
cyclonic structure. Vertical mixing is significant, transporting nutrients
from the lower layer upward and promoting the growth of phyto­
plankton on the sea surface. In summer, driven by the southwest
monsoon, the SCS circulation presents an anticyclonic structure. The
summer monsoon is not as strong as the winter monsoon, which limits
the vertical mixing of the seawater. The increase in the sea surface
temperature is also an crucial factor in the decrease in the Chl-a con­
centration during summer (Chen et al., 2020). The increase in temper­
ature limits the growth and reproduction of phytoplankton; therefore,
the Chl-a content is lower than that in winter. The Chl-a concentrations
were high in 2007 and 2010 when La Niña phenomena occurred and
decreased significantly in 2002, 2006, 2009, and 2015 with the El Niño
phenomena. This is because when an El Niño phenomenon occurs, the
water temperature rises abnormally and the monsoons weaken, which
inhibits the diffusion of nutrients and reduces the amount of phyto­
plankton. When the La Niña phenomenon occurred, the opposite was
observed.
The prediction errors for the three models were calculated (Table 3).
The prediction error of The CNN-LSTM model had the smallest predic­
tion error and provided the best prediction of Chl-a concentrations in the
SCS.
3.2.2. Multi-step prediction
Three models were used to perform 1-year, 2-year, 3-year, 4-year,
and 5-year long-term rolling predictions on the D42 grid area and
calculate the Pearson correlation coefficient between the predicted
value and the true value (Table 4). The larger the Pearson correlation
coefficient, the higher the correlation between the predicted and true
values. The experiments verified that when the time step of the CNNLSTM model was 72, the correlation coefficient between the predicted
value and the true value was the largest, and the prediction performance
was the best. Seferian et al. (2014) determined that primary production
in tropical regions could be predicted three years in advance. Through
the experiments, we concluded that the long-term prediction of Chl-a
concentrations in the SCS can be up to three years.
As the predictions progressed, the Pearson correlation coefficient
decreased exponentially, indicating that the predictions worsen with an
increase in time. The performance of the CNN-LSTM model in predicting
the long-term Chl-a concentrations was significantly better than that of
the LSTM and SARIMA models (Table 4).
3.3. Long-term rolling prediction
We used the CNN-LSTM model to predict the long-term Chl-a se­
quences of all the grid regions. Then, the correlations between the
predicted and true values were calculated (Fig. 9).
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L. Na et al.
Water Research 211 (2022) 118040
Fig. 8. Comparison of three model predictions: (a) CNN-LSTM prediction, (b) LSTM prediction, and (c) SARIMA prediction.
Table 3
Errors predicted by the three models.
MAE (mg/m3)
MSE (mg/m3)
CNN-LSTM
LSTM
SARIMA
6.29e-3
6.80e-5
1.82e-2
5.56e-4
2.34e-2
8.84e-4
Table 4
Long-term rolling prediction performance (r) of the three models in the D42 grid
area.
LSTM
CNN-LSTM
SARIMA
11
1 year
2 year
3 year
4 year
5 year
0.639
0.706
0.235
0.334
0.636
0.181
0.272
0.499
0.245
0.241
0.322
0.215
0.176
0.0248
0.105
L. Na et al.
Water Research 211 (2022) 118040
Fig. 9. Five-year Pearson correlation coefficient when using CNN-LSTM for long-term rolling prediction.
It can be seen from Fig. 9 that the prediction of all the grid areas of
the dataset gradually decreases with an increase in time. In the next
year, the Pearson correlation coefficient exceeds 0.8, indicating that the
predictions for one year are ideal. When predicting the five-year Chl-a
concentration, the Pearson correlation coefficient is reduced to below
0.4, the predicted value and the true value are quite different, and the
prediction accuracy is low. The variation in the prediction performance
for different grid areas indicates the need to fine-tune the model pa­
rameters or identifies the influence of human activities.
To express the level of CNN-LSTM long-term more accurately pre­
dictions, we calculated the regional average of the MAE, MSE, and
Pearson correlation coefficients of the CNN-LSTM long-term predictions
(Table 5).
As the prediction time increased, the average MAE and MSE errors of
the predicted regions gradually increased, and the Pearson correlation
coefficient gradually decreased. The prediction error of the three-year
Chl-a value was relatively small, and the Pearson correlation coeffi­
cient reached 0.5 (Table 5). Therefore, the CNN-LSTM model could
extend the Chl-a prediction to three years. The experiments demon­
strated that the prediction accuracy of the CNN-LSTM model is not only
significantly higher than the LSTM and SARIMA models, but it can also
be applied to long-term rolling predictions.
The study used the M-K trend test method and the least square
method to fit a straight line to analyze the monotonic change trend of the
Chl-a time series. The Chl-a sequence was divided into subsets in units of
years, and the M-K test statistic Z was obtained. The significance level
(α) was set to 0.05, and P is the probability of the statistical trend. Using
the Mann-Kendall trend test method and the least squares method, the
five-year long-term predicted values and true values of all grids in the
study area were analyzed (Table 6).
The M-K test indicates that from 2015 to 2020, the interannual
changes in Chl-a in the study area exhibited a slight upward trend, which
was not significant (P < 1-α). The least square test also established that
Chl-a in the study area demonstrated an upward trend (Table 6). This is
consistent with the results of previous studies (Palacz et al., 2011; Chen
et al., 2014b). From the monotonic trend analysis, it was concluded that
the Chl-a concentration in the central and southern SCS exhibited an
upward trend. The P is relatively low, which may be because the data
were retrieved using remote sensing, and the lack of comparison with
the measured voyage data resulted in a variations between the obtained
data and the true values. In addition, there were many missing data, and
the sequence obtained by interpolation produced errors compared with
the true values.
The trend analysis of a single grid demonstrates that the true Chl-a
concentration of the grids other than the H42 grid exhibited an up­
ward trend. However, the grid trend change in the predicted value has
both upward and downward trends, indicating that the long-term
Table 6
Grid average of the Chl-a concentration trends in the study area.
Table 5
Average performance indicators for the grid.
MAE (mg/m3)
MSE (mg/m3)
r
1 year
2 year
3 year
4 year
5 year
1.94e-2
6.10e-4
6.74e-1
2.02e-2
6.50e-4
5.42e-1
2.18e-2
7.50e-4
4.92e-1
2.38e-2
8.90e-4
3.65e-1
2.68e-2
1.23e-3
2.42e-1
Trend test
M-K test
The least square method
12
Z
P
k
b
True value
Predicted value
9.56e-1
3.65e-1
9.41e-5
1.11e-1
2.67e-1
4.60e-1
2.55e-5
1.19e-1
L. Na et al.
Water Research 211 (2022) 118040
Chl-a concentration prediction experiment was carried out using the
CNN-LSTM model. The long-term prediction of the Chl-a seasonal data
was carried out, and the Chl-a prediction time was extended to three
years.
This research improved the CNN-LSTM model to achieve long-term
predictions on a small sample dataset. The traditional LSTM timeseries prediction model performs poorly on small sample datasets. The
improvement of the CNN-LSTM prediction model in this study can
effectively address this problem. This method is based on the idea of
combining spatio-temporal features of data to expand the dataset, and it
performs well on small sample datasets. Compared with the LSTM and
SARIMA models, the results indicate that the prediction accuracy of the
CNN-LSTM model is much higher than that of the LSTM and SARIMA
models, and the training speed is also faster than that of the two models.
A long-term prediction experiment on Chl-a concentrations was carried
out, and it was found that the Pearson correlation coefficient (r) reached
0.674, 0.542, 0.492 for one year, two years, and three years, respec­
tively. A trend analysis was conducted on the predictive data and true
values from 2015 to 2020, and it was found that the Chl-a concentration
exhibited an upward trend from 2015 to 2020. In addition, the inspec­
tion effect is better for large-scale areas, and the model provides a better
predictions. This indicates that the model may be applicable to largerscale sea areas and has the potential to predict Chl-a concentrations in
global seas.
prediction for the grid is volatile and has certain errors. This may be
because the predicted data were added to the sequence to predict the
next value during the prediction, and the accuracy of the prediction will
decrease annually. In addition, factors such as human activity trajec­
tories and submerged reefs also affect the prediction accuracy of a single
grid area. The Reed Tablemount area is rich in oil and gas resources, and
some oil and gas development activities in the surrounding countries
may affect the predicted level of Chl-a. Areas with more human devel­
opment activities may affect the change in the Chl-a concentration,
leading to the inapplicability and affecting the predictive performance
of the model. Furthermore, there numerous submerged reefs in the Reed
Tablemount, which may affect the distribution of Chl-a in different grid
areas. The long-term prediction of a single grid has errors, but for the
entire region, the long-term prediction of regional Chl-a concentrations
is consistent with the actual situation. Therefore, this study analyzed
whether the model performs better in large-scale predictions than for a
single grid. This results indicate that the model has the potential to be
applied to a wider area, for example, other waters of the SCS, and even
global waters.
From the perspective of the overall study area, both the predicted
and true values show an upward trend on a long-term scale. This in­
dicates that the long-term predictions of Chl-a concentrations are ideal
for the entire study area. The overall Chl-a concentration on the surface
of the Lile Beach exhibited an upward trend. This may be due to the
influence of wind speed (Yu et al., 2020). The annual increase in wind
speed in the SCS promotes the vertical mixing of seawater, which is
beneficial to the growth and reproduction of phytoplankton, thereby
promoting an increase in the Chl-a concentration (Jiang et al., 2019).
Furthermore, a gradual increase in human activities may cause SCS
pollution. The number of red tides in the SCS in recent decades has also
exhibited an upward trend, which is consistent with the upward trend of
the Chl-a concentration. This indicates that the increase in the Chl-a
concentration may be related to the occurrence of HABs events. There­
fore, the long-term prediction of Chl-a has the potential to provide
long-term monitoring and early warning of HABs in the SCS. Due to the
complexity around the SCS, HABs governance in the SCS also has the
characteristics of fragmentation and lag. It is necessary to increase in­
vestments in marine monitoring and predictions, establish long-term
Chl-a marine monitoring and predicting networks, and effectively pro­
vide early warnings of HABs. Chl-a and many other water quality fac­
tors, such as sea temperature, organic carbon, yellow substances, and
heavy metals, constitute vital indicators for marine water quality
monitoring and predictions. Currently, based on the research on the
temporal and spatial changes in Chl-a concentrations, we determined
through some extended experiments that the improved CNN-LSTM
model can also be applied to other seasonal seawater elements. The
next step is to carry out relevant predicting research on other seawater
elements, establish a multi-element coupling model to form a global
long-term prediction system for marine biochemistry, and participate in
global governance.
Declaration of Competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgments
The work is supported by Tianjin Philosophy and Social Science
Planning Project of China (No. TJKS20XSX-015), the National Social
Science Foundation of China (No. 20VHQ002) and the National Natural
Science Foundation of China (No. 41701480).
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.watres.2022.118040.
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