# Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

Authors: Fan Jiang; Jitong Ma; Baosen Wang; Feifei Shen; Lingling Yuan
Journal: Security and Communication Networks
 (2021)

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## Abstract

With the rapid development of maritime technologies, a huge amount of ocean data has been acquired through the state-of-the-art ocean equipment to get better understanding and development of ocean. The prediction and correction of oceanic observation data play a fundamental and important role in the oceanic relevant applications, including both civilian and military fields. On the basis of Argo data, aiming at predicting and correcting the oceanic observation data, we propose an ocean temperature and salinity prediction approach in this paper. In our approach, firstly, bounded nonlinear function is utilized for dataset quality control, which can effectively eliminate the influence of spikes or outliers in Argo data. Then, RBF neural network is used for high-resolution Argo dataset construction. Finally, a bidirectional LSTM framework is proposed to predict and analyze the ocean temperature and salinity on the basis of BOA Argo data. Experimental results demonstrate that the proposed bidirectional LSTM framework can accurately predict the ocean temperature and salinity and enable outstanding performance in oceanic observation data prediction and correction. The proposed approach is also important for the realization of Argo dataset automatic quality control.


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## Body

## 1. Introduction

The ocean covers about three-fourths of the earth’s surface area, and without doubt it is a vital space for human survival and development [1]. In recent years, with the rapid development of ocean economy and the rising awareness of marine environmental protection, the marine science has gained widespread attention. With the development of maritime communications network, aiming at better exploring the marine environment, Argo project is implemented to observe temperature, salinity, and, recently, bio-optical properties in the oceans [2]. As an international project, Argo is an important component of the Global Ocean Observing System, obtaining a snapshot of the physical state of the ocean from 0 to 2000 m every 10 days [3]. In the Argo floats, different kinds of sensors are equipped to collect the oceanic observation data, and the Global Ocean Observing System is regarded as ocean Internet of things. It has been more than 3700 Argo floats in Global Ocean Observing System, which is also Massive Machine-Type Communications for Internet of Things. Through deeply analyzing the Argo dataset, it is helpful for further exploring the internal status or relationship of ocean and developing the relevant marine applications in both civilian and military fields, such as fishery, submarine, and maritime navigation [4].In practice, before Argo floats deployment, the equipped CTD sensors in Argo floats usually need to be calibrated for accurately observing the oceanic data, mainly including salinity, pressure, and temperature [5]. Nevertheless, with time elapsing, the performance of Argo CTD sensors usually degenerates gradually. Besides, due to the impact of biofouling and burn-in, the CTD sensors observed data would drift higher [6, 7]. Another conventional kind of drift of sensors is the conductivity cell thermal mass error. Hence, it is necessary to perform quality control (QC) for Argo observation data. The conventional QC methods can be classified as two levels [8]. One level is real-time QC, which performs a set of agreed automatic checks on all float measurements within the 24–48 hours. The other QC level mainly contains delayed-mode QC, which adopts both statistical analysis and direct scientific examination methods [9]. QC of Argo data involves a complex sequence of both automatic and manual tests to produce data of high scientific quality, which daily consumed numerous resources. Also, in most conventional Argo data QC methods, they usually assume that the collected data obeys normal distributed, while these methods would have poor performance or even fail when it comes to abnormal distribution data. With the rapid development of information technology, lots of researchers start to consider whether machine learning methodology can help ease the burden and improve the efficiency QC process at the same time.Recently, deep learning techniques have played an important role in modelling nonlinear relations from input to output. It is also widely recognized that deep learning techniques have achieved state-of-the-art performance in time-series signal processing, such as nature language and communication signal processing [10]. This encourages researchers to utilize deep learning technology to cope with marine data which usually has the characteristics of diverse types, large amounts, and complicated correlations. Long short-term memory (LSTM) is a specialized model of recurrent neural network which is recognized as a powerful tool for modelling sequential or temporal signals [11]. In particular, with the aid of gate structure inside, LSTM can effectively extract the complicated features from the temporal signals in both short and long terms [12]. Compared with the conventional LSTM, bidirectional LSTM (Bi-LSTM) can run in two directions, and hence it can capture more effectively features in extreme long sequential signals. In Argo dataset, the temperature and salinity curves are exactly this type of temporal signals. Bi-LSTM has the capability of digging this discriminative information from the temperature curve in Argo dataset for prediction.Inspired by the Bi-LSTM network, in this paper, we propose a novel intelligent ocean temperature and salinity data prediction method for Argo data quality control. In this method, firstly, in order to effectively eliminate the influence of spikes or outliers in Argo data, bounded nonlinear function is utilized for dataset quality control. Next, RBF neural network is used for high-resolution Argo dataset construction. Finally, a Bi-LSTM framework is proposed to predict ocean temperature and salinity on the basis of BOA Argo data. Simulation results demonstrate that the proposed Bi-LSTM framework has an outstanding performance in the prediction of temperature and salinity.The rest of this paper is organized as follows. Section2 introduces the related work. Section 3 proposes a Bi-LSTM based prediction method. Section 4 provides the performance evaluation. Section 5 concludes the paper.


## 2. Related Work

The prediction of oceanic observation data can be recognized as a regression problem, which is usually based on the Argo dataset. The Argo project collects information from inside the ocean using a fleet of robotic instruments that move up and down between the surface and a mid-water level. Until April 2021, the number of active Argo profile buoys has reached 3783 in the Global Ocean Observing System, just as shown in Figure1 (http://www.argo.ucsd.edu/).Figure 1
Global Argo floats distribution.The Argo program is a collaborative partnership of more than 30 nations from all continents, and over 1.5 million temperature and salinity profiles have been obtained by using Argo floats all over the world. With the aid of the Argo project, it is possible to accurately predict extreme weather or ocean phenomena as El Nino in the ocean. In [13], the mixed-layer depth of the Southern Ocean was deduced by using the temperature, salinity, and pressure profiles of the Argo floats. Reference [14] conducted the study on the estimation accuracy of Argo profiling float dataset for the temperature and heat storage in North Atlantic Ocean. Reference [15] utilized the unsupervised classification method to cope with Argo temperature profiles. In [16], on the basis of Argo dataset, the spatial variability of the ocean thermohaline fields was estimated in the upper 1400 m ocean layer. In recent years, as the amount of marine data keeps increasing, conventional statistic methods are not suitable to further cope with this dataset. Hence, the machine learning methods are preferred to be adopted for Argo dataset processing. In [17], the machine learning method was used to analyze Argo data and classify different types of thermocline. In [18], the KNN regression method was adopted to predict ocean temperature and salinity. The lateral boundary of thermocline was analyzed and predicted with SVR method by using Argo dataset [19].With the breakthrough of deep learning technology, it has been recognized as one of the popular technologies for temporal signal processing, such as weather forecasting, machine signal, and communication signal processing. LSTM recurrent neural networks (RNN) have been applied into a wide variety of temporal data processing tasks. In [20], on the basis of LSTM-RNN, light-weight and real-time fault detection system was proposed for edge computing in smart factories. Reference [21] proposed a novel method for particulate matter forecasting through combining Convolutional Neural Network (CNN) and LSTM. In [22], an unauthorized broadcasting identification system was developed through LSTM identified framework which has the ability to capture features for dealing with unauthorized broadcasting signals automatically. In [23], a novel LSTM-attention based approach was proposed to predict the travel time and further improve the effectiveness and intelligent of transportation systems. In [24], Bi-LSTM was utilized to noninvasively estimate the right ventricular systolic blood pressure through heart sound signals, and the Bi-LSTM has more effective performance than the conventional LSTM networks.There are also some works which focus on ocean temperature and salinity analysis and QC procedure in recent years. Through using 28 full-depth hydrographic sections, in [25], the result demonstrated that the global mean thermosteric sea level increased at a rate of 0.113–0.100 mm/yr during the 1990s and 2000s. In [26], the response of the thermocline depth to the El Nino-Southern Oscillation was investigated by using the acquired 51-year monthly seawater temperature and surface wind stress data. In [27], on the basis of the sea temperature profiles of China Ocean, the upper thermocline boundaries in the South China Sea were computed; besides the seasonal variation properties of the thermocline were also investigated. Reference [28] developed a semiautomatic QC procedure through objective mapping to remove anomalous values from the profiles. Reference [29] utilized a machine learning approach to the delayed-mode quality control of Argo profiles towards a possible automatic QC system for an Argo data stream. In [30], a deep learning framework was proposed to cope with spatiotemporal ocean sensing data and perform thermocline prediction. This method has superiority in Argo data processing. However, there are few works that focus on the light-weight ocean temperature and salinity prediction and analysis and fast and accurately predict the ocean temperature and salinity remains a challenge. As an alternative and flexible method, in this paper, we attempt to accurately predict ocean temperature and salinity through deep Bi-LSTM learning approach for Argo data quality control.


## 3. Bi-LSTM-Based Observation Data Prediction and Analysis Approach

The temperature and salinity are the vital fundamental components in the study of marine science. In this section, we propose a novel Bi-LSTM based approach to predict ocean temperature and salinity by using Argo dataset. The proposed approach mainly contains two parts, which are Argo dataset preprocessing and Bi-LSTM prediction framework.


### 3.1. Argo Dataset Preprocessing and Reconstruction

In this paper, considering the quality and resolution of Argo dataset, firstly, we propose a bounded nonlinear function to eliminate the influence of spikes in Argo dataset. Next, we utilize the multilayer perception to construct high-resolution dataset for oceanic observation data prediction. In the ocean, as the Argo floats may be affected by some unavoidable ocean phenomenon, the collected Argo data may have some outliers or spikes which are invalid and affect the performance of the proposed ocean temperature and salinity prediction approach. Hence, we design a novel bounded nonlinear function to alleviate the deterioration caused by these spikes, which is defined as(1)gy=ζ⋅λ1⋅y−a1+λ2⋅y−a2+a,where ζ,a,λ1, and λ2 are tunable parameters. It can be known from equation (1) that gy is a bounded function. When y∉−y0,y0, gy is a nonlinear function; when y∈−y0,y0, gy≃y presents asymptotic linearity. In the nonlinear zone, BNF can effectively suppress the outliers or spikes via the nonlinear boundedness. Meanwhile, in the asymptotically linear zone, BNF can maintain the original value via linearity. Thus, we adopt the designed BNF to cope with the collected data by Argo floats to further complete quality control.Following the BNF, the radial basis function (RBF) neural network is adopted to construct high-resolution Argo dataset, which has the advantage in matching the relationships between the inputs and outputs. In the designed RBF neural network, there are an input layer, an output layer, andn hidden layers with p hidden neurons. To further explain the designed RBF neural network, we employ A to represent the historical observation matrix, and A contains two parts, spatiotemporal feature submatrix X and variable submatrix S. Besides, we utilize x,t∈X to represent the piece of space-time coordinate and employ sx,t∈S to represent the oceanographic feature. In the operation of Argo dataset construction, the space-time coordinate x,t=xlon,xlat,xdep,t, where xlon denotes the longitude, xlat denotes the latitude, xdep denotes the depth, and t denotes the month order in Argo dataset. Moreover, the corresponding variable set sx,t=stem,ssat, where stem denotes the temperature and ssat denotes the salinity. Thus, the relationship among A, X, and S can be denoted as(2)A=X,S,where X is the input matrix which contains n×p elements where n denotes the number of observations and p denotes the number of spatiotemporal features in an observation. Besides, S is the variable submatrix which consists of n×q elements, in which q denotes the number of oceanographic features. RBF neural networks can learn the mapping relationship between the output variable submatrix S and the input spatiotemporal submatrix X. The radial basis function fx,ci in neural network is formulated as(3)fx,ci=e−μix−ci,where ci denotes the centre of the ith hidden layer neurons and μi is the variable parameter. The parameters in RBF neural network are trained according to the input coordinates and their corresponding output variables. The training process of RBF neural network mainly contains two steps. Random sampling method is adopted to compute ci at first, and then back-propagation method is adopted to determine μi and the weights of RBF neural network. After RBF neural network training, the optimal nonlinear transformation function can be obtained, and, on the basis of this function, we can further get the predicted output to conduct high-resolution dataset which will be utilized to perform temperature and salinity prediction.


### 3.2. Bi-LSTM Prediction Framework

Aiming at accurately predicting the temperature and salinity, following the Argo data processing, we further propose a Bi-LSTM based ocean temperature and salinity prediction approach. In the proposed approach, the LSTM recurrent network can effectively cope with the vanishing gradient problem in RNNs and meanwhile maintain the advantage of RNNs in tackling time-series learning problem. This is because LSTM defines the cell state to capture the complicated correlation features within time series. As shown in Figure2, it is the structure of LSTM cell, which mainly consists of the forget gate f, input gate i, and output gate o. The formulations of LSTM network are given as follows:where Ct and ht denote the cell status and output of LSTM at moment t. Wf, Wi, WC, Wo, bf, bi, bC, and bo denote the weight and bias matrices; σ and tanh denote sigmoid and tank activation functions, respectively. According to the outputs of the previous steps and inputs of the present step, the status of LSTM cell interacts with the intermediate output and the following input to further decide the operation for the elements of the internal state vector, including update, maintain, and vanish the state.Figure 2
An LSTM cell.On the basis of LSTM cell, we propose Bi-LSTM framework for the temperature and salinity prediction, which is on the basis of the basic LSTM cell. Besides, the proposed Bi-LSTM framework mainly includes forward and backward channels, and both channels can acquire the past and future information at the same time. The proposed Bi-LSTM network can extract deep and robust features for temporal signal prediction. In the designed Bi-LSTM framework, the information can be processed in both the forward and backward directions through two different hidden layers and fed forward to the output layer. The proposed Bi-LSTM can make full use of the information of input data in forward states, backward states, and short and long terms. Hence, the proposed Bi-LSTM framework has capability of accurately predicting the temperature and salinity with Argo dataset.As shown in Figure3, it is the proposed Bi-LSTM prediction network framework. Apparently, it can be known from Figure 3 that the prediction network mainly consists of one input layer, one forward LSTM layer, one backward LSTM layer, and the output layer. In the input layer, the high-resolution Argo data, which is reconstructed by RBF network, is employed as the input of this network, and the output of this network is the prediction results. After the input layer, the information is processed in both forward and backward directions with two different hidden layers and fed forward to an output layer. Hence, the proposed prediction network can effectively utilize both the past information (through forward states) and the future inputs (through back states) for time-series prediction.Figure 3
Bi-LSTM prediction network framework.(4)ft=σWf⋅ht−1,xt+bf,it=σWi⋅ht−1,xt+bi,Ct=ft⋅Ct−1+it⋅tanhWC⋅ht−1,xt+bc,ot=σWo⋅ht−1,xt+bo,ht=ot⋅tanhCt.


## 3.1. Argo Dataset Preprocessing and Reconstruction

In this paper, considering the quality and resolution of Argo dataset, firstly, we propose a bounded nonlinear function to eliminate the influence of spikes in Argo dataset. Next, we utilize the multilayer perception to construct high-resolution dataset for oceanic observation data prediction. In the ocean, as the Argo floats may be affected by some unavoidable ocean phenomenon, the collected Argo data may have some outliers or spikes which are invalid and affect the performance of the proposed ocean temperature and salinity prediction approach. Hence, we design a novel bounded nonlinear function to alleviate the deterioration caused by these spikes, which is defined as(1)gy=ζ⋅λ1⋅y−a1+λ2⋅y−a2+a,where ζ,a,λ1, and λ2 are tunable parameters. It can be known from equation (1) that gy is a bounded function. When y∉−y0,y0, gy is a nonlinear function; when y∈−y0,y0, gy≃y presents asymptotic linearity. In the nonlinear zone, BNF can effectively suppress the outliers or spikes via the nonlinear boundedness. Meanwhile, in the asymptotically linear zone, BNF can maintain the original value via linearity. Thus, we adopt the designed BNF to cope with the collected data by Argo floats to further complete quality control.Following the BNF, the radial basis function (RBF) neural network is adopted to construct high-resolution Argo dataset, which has the advantage in matching the relationships between the inputs and outputs. In the designed RBF neural network, there are an input layer, an output layer, andn hidden layers with p hidden neurons. To further explain the designed RBF neural network, we employ A to represent the historical observation matrix, and A contains two parts, spatiotemporal feature submatrix X and variable submatrix S. Besides, we utilize x,t∈X to represent the piece of space-time coordinate and employ sx,t∈S to represent the oceanographic feature. In the operation of Argo dataset construction, the space-time coordinate x,t=xlon,xlat,xdep,t, where xlon denotes the longitude, xlat denotes the latitude, xdep denotes the depth, and t denotes the month order in Argo dataset. Moreover, the corresponding variable set sx,t=stem,ssat, where stem denotes the temperature and ssat denotes the salinity. Thus, the relationship among A, X, and S can be denoted as(2)A=X,S,where X is the input matrix which contains n×p elements where n denotes the number of observations and p denotes the number of spatiotemporal features in an observation. Besides, S is the variable submatrix which consists of n×q elements, in which q denotes the number of oceanographic features. RBF neural networks can learn the mapping relationship between the output variable submatrix S and the input spatiotemporal submatrix X. The radial basis function fx,ci in neural network is formulated as(3)fx,ci=e−μix−ci,where ci denotes the centre of the ith hidden layer neurons and μi is the variable parameter. The parameters in RBF neural network are trained according to the input coordinates and their corresponding output variables. The training process of RBF neural network mainly contains two steps. Random sampling method is adopted to compute ci at first, and then back-propagation method is adopted to determine μi and the weights of RBF neural network. After RBF neural network training, the optimal nonlinear transformation function can be obtained, and, on the basis of this function, we can further get the predicted output to conduct high-resolution dataset which will be utilized to perform temperature and salinity prediction.


## 3.2. Bi-LSTM Prediction Framework

Aiming at accurately predicting the temperature and salinity, following the Argo data processing, we further propose a Bi-LSTM based ocean temperature and salinity prediction approach. In the proposed approach, the LSTM recurrent network can effectively cope with the vanishing gradient problem in RNNs and meanwhile maintain the advantage of RNNs in tackling time-series learning problem. This is because LSTM defines the cell state to capture the complicated correlation features within time series. As shown in Figure2, it is the structure of LSTM cell, which mainly consists of the forget gate f, input gate i, and output gate o. The formulations of LSTM network are given as follows:where Ct and ht denote the cell status and output of LSTM at moment t. Wf, Wi, WC, Wo, bf, bi, bC, and bo denote the weight and bias matrices; σ and tanh denote sigmoid and tank activation functions, respectively. According to the outputs of the previous steps and inputs of the present step, the status of LSTM cell interacts with the intermediate output and the following input to further decide the operation for the elements of the internal state vector, including update, maintain, and vanish the state.Figure 2
An LSTM cell.On the basis of LSTM cell, we propose Bi-LSTM framework for the temperature and salinity prediction, which is on the basis of the basic LSTM cell. Besides, the proposed Bi-LSTM framework mainly includes forward and backward channels, and both channels can acquire the past and future information at the same time. The proposed Bi-LSTM network can extract deep and robust features for temporal signal prediction. In the designed Bi-LSTM framework, the information can be processed in both the forward and backward directions through two different hidden layers and fed forward to the output layer. The proposed Bi-LSTM can make full use of the information of input data in forward states, backward states, and short and long terms. Hence, the proposed Bi-LSTM framework has capability of accurately predicting the temperature and salinity with Argo dataset.As shown in Figure3, it is the proposed Bi-LSTM prediction network framework. Apparently, it can be known from Figure 3 that the prediction network mainly consists of one input layer, one forward LSTM layer, one backward LSTM layer, and the output layer. In the input layer, the high-resolution Argo data, which is reconstructed by RBF network, is employed as the input of this network, and the output of this network is the prediction results. After the input layer, the information is processed in both forward and backward directions with two different hidden layers and fed forward to an output layer. Hence, the proposed prediction network can effectively utilize both the past information (through forward states) and the future inputs (through back states) for time-series prediction.Figure 3
Bi-LSTM prediction network framework.(4)ft=σWf⋅ht−1,xt+bf,it=σWi⋅ht−1,xt+bi,Ct=ft⋅Ct−1+it⋅tanhWC⋅ht−1,xt+bc,ot=σWo⋅ht−1,xt+bo,ht=ot⋅tanhCt.


## 4. Experiments and Discussions

In this section, the performances of the proposed thermocline prediction approach based on Bi-LSTM neural network are evaluated through extensive experiments with the Argo dataset. Firstly, the adopted Argo dataset is introduced. Then the thermocline prediction performances of the proposed method and other contrastive methods are compared, and the experimental results are also further analyzed and discussed. Besides, the proposed Bi-LSTM approach is implemented with Python based on TensorFlow and keras libraries. The experiment adopts the desktop computer with NVIDA RTX 3090 GPU as testbed, and the batch normalization is adopted to speed up the training speed where Adam optimizer is used for training Bi-LSTM network.


### 4.1. Argo Dataset

The spatial range of Global Ocean Argo gridded dataset (BOA Argo) covers the global ocean79.5°S−79.5°N,180°W−180°E with a spatial resolution of 1°×1°. The seawater between 0 and 1975 m in depth is divided into 58 vertical standard layers, and the minimum distance between the two layers is 10 m. In this paper, we select the Argo dataset with a depth less than 1975 m for further analysis, and this dataset has completed quality control. In particular, as shown in Figure 4, the Argo dataset of the ocean area is adopted for ocean temperature and salinity prediction and analysis. We choose this area for research as it has intense ocean mixing. It is important to study the vertical variation of temperature and salinity for the study of ocean mixing. For example, as shown in Figure 5, it can be known that, in the selected ocean area, the salinity changes dramatically over time with the depth of 200 m, and there is significant salinity jump with the depth from 400 m to 600 m; besides, the salinity is also related to the time at the depth of 1000 m. Hence, it is necessary to further study the selected region.Figure 4
The distribution of selected Argo floats.Figure 5
The salinity of selected ocean region.


### 4.2. Performance of Temperature and Salinity Prediction

In this section, we utilize mean absolute percentage error (MAPE) and root mean square error (RMSE) to evaluate the prediction performance of the proposed Bi-LSTM approach, which can be formulated as(5)MAEs,s^=1m∑i=0m−1si−s^i,RMSE=∑i=1msi−s^i2m,where si and s^i represent the real value and generated value of the ith sample, while s¯ denotes the mean value of all data. Besides, a two-layer Bi-LSTM and a regression output layer are used for ocean observation data prediction. In the proposed Bi-LSTM network, the weights are initialized with random normal distribution, and the mean square error is adopted as the loss function. Moreover, we take the prediction approach based on the multiscale singular spectrum analysis least squares-support vector machines (MSSA-LSSVM), the recurrent neural network (RNN), and the conventional LSTM network as the comparative methods in the experiments.


#### 4.2.1. Prediction Results of Temperature

In this experiment, with regard to temperature prediction, the temperature data collected by Argo floats from January 2011 to December 2018 in the experimental area are selected as training set, and the Argo data from January 2018 to December 2019 are employed as testing set. The Argo temperature data from January 2020 to December 2020 are utilized as validation set. In the training stage, the learning rate of Adam optimizer is set as 0.001 for gradient descent learning, the batch size is set as 32, and the training epochs are 1100. These parameters are obtained by trial and error according to the prediction performance.To evaluate the temperature prediction performance, Table1 gives the error statistics of the proposed and comparative prediction methods. As depicted in Table 1, it is apparent that both the MAE and RMSE of the MSSA-LSSVM, RNN, and LSTM based predication methods are all higher than the proposed Bi-LSTM prediction. In other words, the comparative prediction methods have greater prediction errors in both the average magnitude and residuals dispersion compared to the proposed Bi-LSTM method. Hence, the proposed method outperforms the other comparative methods on both MAE and RMSE metrics. To further demonstrate the performance of the proposed Bi-LSTM prediction method, as shown in Figure 6, we have the predicted temperature curve and the real temperature curve from January 2020 to December 2020 at the position of 19.5°N and 155.5°E with depth of 0–1975 m for comparison. In this figure, the blue line denotes the predicted temperature curve and the red line denotes the real temperature curve of Argo dataset. In addition, we also compare the real oceanic temperature and the predicted temperature in the selected area 79.5°S−79.5°N,180°W−180°E for further evaluation, which are denoted as the blue line and light blue line in Figure 7, respectively. Obviously, it can be known from both Figures 6 and 7 that the predicted temperature is almost coincided with that of real temperature curve in Argo dataset. Thus, the proposed Bi-LSTM method can accurately predict the temperature.Table 1
Statistics results for temperature prediction.
MetricsMSSA-LSSVMRNNLSTMBi-LSTMMAE0.0230.01810.01730.0116RMSE0.0340.02690.02580.0185Bold values show the performance of our method.Figure 6
Comparison of predicted and real temperature of 2020.Figure 7
Comparison of predicted temperature and real temperature of selected area.


#### 4.2.2. Prediction Results of Salinity

For the salinity prediction, we utilize the salinity data collected by Argo floats from January 2011 to December 2018 in the position of12.5°N−21.5°N,155.5°E−169.5°E as training set, and the Argo salinity data from January 2018 to December 2019 are used as testing set. Besides, the Argo salinity data from January 2020 to December 2020 are utilized as validation set. The parameters of Bi-LSTM prediction network are the same as those in the last experiment.Table2 gives the statistics results for salinity prediction to evaluate the performance of proposed and comparative salinity prediction methods. As shown in Table 2, it is obvious that, compared to the MSSA-LSSVM, RNN, and LSTM based salinity prediction method, the proposed Bi-LSTM method has a lower MAE and RMSE. Similarity to the explanation of temperature prediction experiment, it can be known that the proposed Bi-LSTM method has an outstanding performance in salinity prediction. Also, as shown in Figure 7, we compare the predicted salinity and the real salinity to further evaluate the salinity performance of the proposed Bi-LSTM method. As depicted in Figure 8, the blue line is the predicted salinity curve which is predicted based on the historical observation Argo data, and the red line is the real salinity curve which is collected by Argo floats from January 2020 to December 2020 at the position of 19.5°N and 155.5°E within the depth 0–1975 m. Moreover, we also compare the real oceanic salinity and the predicted salinity in the selected area 79.5°S−79.5°N,180°W−180°E for further evaluation, which are denoted as the blue line and light blue line in Figure 9, respectively. It is apparent in both Figures 8 and 9 that the predicted salinity is almost coincided with that of real salinity curve. Hence, the proposed Bi-LSTM method enables superior performance in salinity prediction.Table 2
Statistics results for salinity prediction.
MetricsMSSA-LSSVMRNNLSTMBi-LSTMMAE0.0180.0130.01010.0076RMSE0.0260.0180.0160.0093Bold values show the performance of our method.Figure 8
Comparison of predicted salinity and real salinity of 2020.Figure 9
Comparison of predicted salinity and real salinity of selected area.In addition, through combining the well-trained Bi-LSTM prediction network, we can conduct the Argo data real-time correction for Argo data quality control. Comparing the prediction values with the acquired data by Argo floats, if there is large deviation or outliers, we can directly deal with or pay close attention to these outliers. Besides, on the basis of temperature and salinity prediction, we can further offer a more accurate reference for quality control in practice.


## 4.1. Argo Dataset

The spatial range of Global Ocean Argo gridded dataset (BOA Argo) covers the global ocean79.5°S−79.5°N,180°W−180°E with a spatial resolution of 1°×1°. The seawater between 0 and 1975 m in depth is divided into 58 vertical standard layers, and the minimum distance between the two layers is 10 m. In this paper, we select the Argo dataset with a depth less than 1975 m for further analysis, and this dataset has completed quality control. In particular, as shown in Figure 4, the Argo dataset of the ocean area is adopted for ocean temperature and salinity prediction and analysis. We choose this area for research as it has intense ocean mixing. It is important to study the vertical variation of temperature and salinity for the study of ocean mixing. For example, as shown in Figure 5, it can be known that, in the selected ocean area, the salinity changes dramatically over time with the depth of 200 m, and there is significant salinity jump with the depth from 400 m to 600 m; besides, the salinity is also related to the time at the depth of 1000 m. Hence, it is necessary to further study the selected region.Figure 4
The distribution of selected Argo floats.Figure 5
The salinity of selected ocean region.


## 4.2. Performance of Temperature and Salinity Prediction

In this section, we utilize mean absolute percentage error (MAPE) and root mean square error (RMSE) to evaluate the prediction performance of the proposed Bi-LSTM approach, which can be formulated as(5)MAEs,s^=1m∑i=0m−1si−s^i,RMSE=∑i=1msi−s^i2m,where si and s^i represent the real value and generated value of the ith sample, while s¯ denotes the mean value of all data. Besides, a two-layer Bi-LSTM and a regression output layer are used for ocean observation data prediction. In the proposed Bi-LSTM network, the weights are initialized with random normal distribution, and the mean square error is adopted as the loss function. Moreover, we take the prediction approach based on the multiscale singular spectrum analysis least squares-support vector machines (MSSA-LSSVM), the recurrent neural network (RNN), and the conventional LSTM network as the comparative methods in the experiments.


### 4.2.1. Prediction Results of Temperature

In this experiment, with regard to temperature prediction, the temperature data collected by Argo floats from January 2011 to December 2018 in the experimental area are selected as training set, and the Argo data from January 2018 to December 2019 are employed as testing set. The Argo temperature data from January 2020 to December 2020 are utilized as validation set. In the training stage, the learning rate of Adam optimizer is set as 0.001 for gradient descent learning, the batch size is set as 32, and the training epochs are 1100. These parameters are obtained by trial and error according to the prediction performance.To evaluate the temperature prediction performance, Table1 gives the error statistics of the proposed and comparative prediction methods. As depicted in Table 1, it is apparent that both the MAE and RMSE of the MSSA-LSSVM, RNN, and LSTM based predication methods are all higher than the proposed Bi-LSTM prediction. In other words, the comparative prediction methods have greater prediction errors in both the average magnitude and residuals dispersion compared to the proposed Bi-LSTM method. Hence, the proposed method outperforms the other comparative methods on both MAE and RMSE metrics. To further demonstrate the performance of the proposed Bi-LSTM prediction method, as shown in Figure 6, we have the predicted temperature curve and the real temperature curve from January 2020 to December 2020 at the position of 19.5°N and 155.5°E with depth of 0–1975 m for comparison. In this figure, the blue line denotes the predicted temperature curve and the red line denotes the real temperature curve of Argo dataset. In addition, we also compare the real oceanic temperature and the predicted temperature in the selected area 79.5°S−79.5°N,180°W−180°E for further evaluation, which are denoted as the blue line and light blue line in Figure 7, respectively. Obviously, it can be known from both Figures 6 and 7 that the predicted temperature is almost coincided with that of real temperature curve in Argo dataset. Thus, the proposed Bi-LSTM method can accurately predict the temperature.Table 1
Statistics results for temperature prediction.
MetricsMSSA-LSSVMRNNLSTMBi-LSTMMAE0.0230.01810.01730.0116RMSE0.0340.02690.02580.0185Bold values show the performance of our method.Figure 6
Comparison of predicted and real temperature of 2020.Figure 7
Comparison of predicted temperature and real temperature of selected area.


### 4.2.2. Prediction Results of Salinity

For the salinity prediction, we utilize the salinity data collected by Argo floats from January 2011 to December 2018 in the position of12.5°N−21.5°N,155.5°E−169.5°E as training set, and the Argo salinity data from January 2018 to December 2019 are used as testing set. Besides, the Argo salinity data from January 2020 to December 2020 are utilized as validation set. The parameters of Bi-LSTM prediction network are the same as those in the last experiment.Table2 gives the statistics results for salinity prediction to evaluate the performance of proposed and comparative salinity prediction methods. As shown in Table 2, it is obvious that, compared to the MSSA-LSSVM, RNN, and LSTM based salinity prediction method, the proposed Bi-LSTM method has a lower MAE and RMSE. Similarity to the explanation of temperature prediction experiment, it can be known that the proposed Bi-LSTM method has an outstanding performance in salinity prediction. Also, as shown in Figure 7, we compare the predicted salinity and the real salinity to further evaluate the salinity performance of the proposed Bi-LSTM method. As depicted in Figure 8, the blue line is the predicted salinity curve which is predicted based on the historical observation Argo data, and the red line is the real salinity curve which is collected by Argo floats from January 2020 to December 2020 at the position of 19.5°N and 155.5°E within the depth 0–1975 m. Moreover, we also compare the real oceanic salinity and the predicted salinity in the selected area 79.5°S−79.5°N,180°W−180°E for further evaluation, which are denoted as the blue line and light blue line in Figure 9, respectively. It is apparent in both Figures 8 and 9 that the predicted salinity is almost coincided with that of real salinity curve. Hence, the proposed Bi-LSTM method enables superior performance in salinity prediction.Table 2
Statistics results for salinity prediction.
MetricsMSSA-LSSVMRNNLSTMBi-LSTMMAE0.0180.0130.01010.0076RMSE0.0260.0180.0160.0093Bold values show the performance of our method.Figure 8
Comparison of predicted salinity and real salinity of 2020.Figure 9
Comparison of predicted salinity and real salinity of selected area.In addition, through combining the well-trained Bi-LSTM prediction network, we can conduct the Argo data real-time correction for Argo data quality control. Comparing the prediction values with the acquired data by Argo floats, if there is large deviation or outliers, we can directly deal with or pay close attention to these outliers. Besides, on the basis of temperature and salinity prediction, we can further offer a more accurate reference for quality control in practice.


## 4.2.1. Prediction Results of Temperature

In this experiment, with regard to temperature prediction, the temperature data collected by Argo floats from January 2011 to December 2018 in the experimental area are selected as training set, and the Argo data from January 2018 to December 2019 are employed as testing set. The Argo temperature data from January 2020 to December 2020 are utilized as validation set. In the training stage, the learning rate of Adam optimizer is set as 0.001 for gradient descent learning, the batch size is set as 32, and the training epochs are 1100. These parameters are obtained by trial and error according to the prediction performance.To evaluate the temperature prediction performance, Table1 gives the error statistics of the proposed and comparative prediction methods. As depicted in Table 1, it is apparent that both the MAE and RMSE of the MSSA-LSSVM, RNN, and LSTM based predication methods are all higher than the proposed Bi-LSTM prediction. In other words, the comparative prediction methods have greater prediction errors in both the average magnitude and residuals dispersion compared to the proposed Bi-LSTM method. Hence, the proposed method outperforms the other comparative methods on both MAE and RMSE metrics. To further demonstrate the performance of the proposed Bi-LSTM prediction method, as shown in Figure 6, we have the predicted temperature curve and the real temperature curve from January 2020 to December 2020 at the position of 19.5°N and 155.5°E with depth of 0–1975 m for comparison. In this figure, the blue line denotes the predicted temperature curve and the red line denotes the real temperature curve of Argo dataset. In addition, we also compare the real oceanic temperature and the predicted temperature in the selected area 79.5°S−79.5°N,180°W−180°E for further evaluation, which are denoted as the blue line and light blue line in Figure 7, respectively. Obviously, it can be known from both Figures 6 and 7 that the predicted temperature is almost coincided with that of real temperature curve in Argo dataset. Thus, the proposed Bi-LSTM method can accurately predict the temperature.Table 1
Statistics results for temperature prediction.
MetricsMSSA-LSSVMRNNLSTMBi-LSTMMAE0.0230.01810.01730.0116RMSE0.0340.02690.02580.0185Bold values show the performance of our method.Figure 6
Comparison of predicted and real temperature of 2020.Figure 7
Comparison of predicted temperature and real temperature of selected area.


## 4.2.2. Prediction Results of Salinity

For the salinity prediction, we utilize the salinity data collected by Argo floats from January 2011 to December 2018 in the position of12.5°N−21.5°N,155.5°E−169.5°E as training set, and the Argo salinity data from January 2018 to December 2019 are used as testing set. Besides, the Argo salinity data from January 2020 to December 2020 are utilized as validation set. The parameters of Bi-LSTM prediction network are the same as those in the last experiment.Table2 gives the statistics results for salinity prediction to evaluate the performance of proposed and comparative salinity prediction methods. As shown in Table 2, it is obvious that, compared to the MSSA-LSSVM, RNN, and LSTM based salinity prediction method, the proposed Bi-LSTM method has a lower MAE and RMSE. Similarity to the explanation of temperature prediction experiment, it can be known that the proposed Bi-LSTM method has an outstanding performance in salinity prediction. Also, as shown in Figure 7, we compare the predicted salinity and the real salinity to further evaluate the salinity performance of the proposed Bi-LSTM method. As depicted in Figure 8, the blue line is the predicted salinity curve which is predicted based on the historical observation Argo data, and the red line is the real salinity curve which is collected by Argo floats from January 2020 to December 2020 at the position of 19.5°N and 155.5°E within the depth 0–1975 m. Moreover, we also compare the real oceanic salinity and the predicted salinity in the selected area 79.5°S−79.5°N,180°W−180°E for further evaluation, which are denoted as the blue line and light blue line in Figure 9, respectively. It is apparent in both Figures 8 and 9 that the predicted salinity is almost coincided with that of real salinity curve. Hence, the proposed Bi-LSTM method enables superior performance in salinity prediction.Table 2
Statistics results for salinity prediction.
MetricsMSSA-LSSVMRNNLSTMBi-LSTMMAE0.0180.0130.01010.0076RMSE0.0260.0180.0160.0093Bold values show the performance of our method.Figure 8
Comparison of predicted salinity and real salinity of 2020.Figure 9
Comparison of predicted salinity and real salinity of selected area.In addition, through combining the well-trained Bi-LSTM prediction network, we can conduct the Argo data real-time correction for Argo data quality control. Comparing the prediction values with the acquired data by Argo floats, if there is large deviation or outliers, we can directly deal with or pay close attention to these outliers. Besides, on the basis of temperature and salinity prediction, we can further offer a more accurate reference for quality control in practice.


## 5. Conclusion

In this paper, we propose a novel intelligent ocean temperature and salinity data prediction method for Argo dataset quality control. Our design enables effectively eliminating the influence of spikes or outliers in Argo data through the bounded nonlinear function. Besides, the high-resolution Argo dataset is constructed by the proposed RBF neural network. Further, an Bi-LSTM framework is proposed to predict and analyze the temperature and salinity on the basis of BOA Argo data. Moreover, we performed the proposed Bi-LSTM prediction approach using the collected Argo dataset as training, testing, and validation dataset and demonstrated via cross-validation that it has an outstanding performance in both the temperature and salinity prediction through experimental results. Besides, based on the predicted temperature and salinity curves, it can provide a tool to classify the bias by the level of consistence in QC. Hence, the proposed prediction method provides a new thought for real-time QC, and, for the bias from real value and prediction, we may offer the tag to the scientist who is in charge of delay mode QC. In the future, we will develop data diversity on the basis of the prediction of temperature and salinity data, and then we can offer the more accurate reference for delay mode QC approach.



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*Source: 5665386-2021-10-11.xml*