A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment

Abstract

Great changes have been brought about by the coastal environment when the economy develops rapidly. Coastal environmental monitoring is the basis and technical guarantee for coastal environmental protection supervision and management. It is one of the important tasks to detect and timely discover coastal seawater anomalies. Usually, a single sensor cannot determine whether the coastal environment or ship operation is an anomaly. Recently, an unmanned surface vehicle for coastal environment monitoring was developed, and stacked autoencoders are used for seawater anomaly detection using multisensor data fusion methods. The multisensor data of pH, conductivity, and ammonia nitrogen are employed to judge the anomaly of seawater. The mean, standard deviation, mean square root, and normalized power spectrum features of multisensor data are extracted, and a stacked autoencoder is employed to fuse these features for anomaly detection. The proposed method is feasible and effective for anomaly detection of coastal water quality and ship operation. Compared with other commonly used methods, the proposed method has a higher recall, precision, and F1 score performance.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The coastal environment is closely related to human life. With the rapid economic development around the world, great changes have been brought about in the coastal environment. In particular, the increasing environmental degradation, including coastal environmental pollution, is increasingly threatening human health. Environmental monitoring has always been one of the most effective ways for mankind to control environmental pollution, and it is also an essential key step in environmental governance. Coastal environmental monitoring is the basis and technical guarantee for coastal environmental protection supervision and management. The types and concentrations of pollutants in coastal waters can be measured and compared through real-time monitoring of the coastal environment, which can provide scientific and quantitative data for coastal environmental protection. In recent years, more and more automated methods for real-time monitoring of water quality have been studied. Anomaly detection of different types of water quality has been studied [1–7], some of the main types of research literature are listed in Table 1, and the corresponding main methods are also listed in the table. Artificial intelligence methods are increasingly used in these studies. Also, there are few studies on the quality anomaly detection of water in coastal environments. This automatic monitoring is completed by various sensors. Usually, a single sensor cannot determine whether the coastal environment is abnormal and whether the ship’s state is abnormal [8–11]. How to comprehensively use these sensors to provide environmental anomaly detection and anomaly detection of ship equipment itself is a practical problem that needs to be solved.

Table 1

Anomaly detection for different water types.

Content	Anomaly detection method
Groundwater level	Artificial neural network [1]
Surface water quality	Bayesian autoregressive [2]
Aquaculture water	Density clustering [3]
Wastewater	One-class classifier [4]
Drinking water	Direct measurement [5]

Recently, an unmanned surface vehicle (USV) for coastal environment monitoring was successfully developed by the author team [8–10]. A data platform was developed and deployed on the ship [11]. The data platform stores various sensor data in a distributed manner. These sensors collect a large amount of data on coastal environment data and ship power, propulsion, and navigation data. The ship mainly completes regular ecological monitoring of water quality and pollution. Conduct surveillance monitoring of important continental source sewage outlets and coastal projects along the coast. The ship is equipped with biological testing, microbial testing, hydrological measurement, and water quality monitoring equipment. The ship is equipped with a multiparameter water quality analyzer, flow meter, and automatic sampler with a volume of 5L. The marine hydrological parameters that the ship can monitor include wind speed, direction, flow rate, flow direction, ambient temperature, and atmospheric pressure. Water quality biological state measurements include solvent oxygen, pH value, salinity, and various nitrates. It is also equipped with an acoustic doppler current profiler (ADCP) and single beam side-scan sonar. The ship is equipped with a global positioning system (GPS), video surveillance, automatic navigation, and an intelligent data platform, which can upload data to the shore data center. It has the functions of autonomous navigation, and it supports all navigation and sampling tasks through remote intelligent control. The ducted propeller is used to effectively waterproof grass, branches, fishing nets, and other foreign objects.

Water quality monitoring is an important part of coastal environmental monitoring. An anomaly means that the data monitored by the sensor does not conform to the usual pattern. One situation is that the value of the seawater quality index exceeds the normal range, and the other is that the change of the data does not conform to the change law of the context, but the value does not exceed the normal range. The former is easy to judge, while the latter is more difficult to detect. Generally, abnormal seawater quality cannot be judged by single-sensor data. Stacked autoencoders are used for seawater anomaly detection using multisensor data fusion methods. The multisensor data of pH, conductivity, and ammonia nitrogen are fused to judge the anomaly of seawater. As a kind of autonomous navigation equipment, an unmanned vessel’s anomaly detection of equipment and navigation data is also one of the necessary functions. This paper also studies multisensor anomaly detection based on power propulsion and navigation data in an unmanned ship based on an autoencoder. Artificial intelligence (AI) has been rapidly developed, and AI has made great progress in recent years and has achieved great success in the fields of classification, recognition, and anomaly detection [12–17]. Autoencoder is an unsupervised machine learning method of AI. Autoencoder is employed for anomaly detection by multisensor data fusion in this paper.

The innovation of this research is as follows. Firstly, a fusion method employing stacked autoencoders is proposed, and the method is applied to anomaly detection. Secondly, anomaly detection applications for coastal water quality and the operation of UAVs are described. Compared with other commonly used methods, the proposed method has a higher recall, precision, and F1 score performance.

2. Methods

Autoencoder has received extensive attention in the field of machine learning and has been successfully used in data dimensionality reduction, feature extraction, fault diagnosis, and other fields. Autoencoder is trained to learn a reconstruction close to its original input. By using the hidden representation of the autoencoder as the input of another autoencoder, the autoencoders are stacked to form a deep autoencoder. Anomaly detection based on an autoencoder is a semisupervised learning method based on deviation, which is especially suitable for tasks such as anomaly detection. In the anomaly detection task, there are a lot of normal data, and the sample data of anomaly data are less, which is not statistically significant. Stacked autoencoders that combine deep networks and autoencoders can better realize the effective expression of data in low-dimensional feature spaces through hierarchical learning [13–17].

2.1. Autoencoder

The architecture of the standard autoencoder is illustrated in Figure 1. It has a fully symmetrical network architecture. The network is symmetrical with the hidden layer as the center, and the two sides of the network are the input and output layers. When the hidden layer in the middle is used as the axis of symmetry, an encoder, $h=fX$ and a decoder, $y=fh$ are formed with the preceding input layer and the subsequent output layer, respectively. In which, the input is $X$, the hidden layer weight is $h$, the output is $y$, and the activation function is f.

[figure(s) omitted; refer to PDF]

The hidden layer in the autoencoder can be regarded as a feature layer, especially when the hidden layer node number is smaller than the node number of the input and output layers, the autoencoder is called an under-complete autoencoder. At this time, the autoencoder forces the learning of the low-dimensional representation of the input data to capture the most significant features in the data. In particular, when a linear activation function is used, the under-complete autoencoder performs a linear transformation. It is equivalent to linear dimensionality reduction approaches, such as principal component analysis (PCA) and singular value decomposition (SVD) [18]. Denote weights as W and bias as b, the encoder can be expressed as $z=W_{E}X+b_E$, and the decoder as $z=W_{D}X+b_D$. Autoencoder obtains the feature expression through the minimum error between the output and input obtained by reconstruction. The error function is defined as the second norm of input and output, which is$$\begin{array}{}\text{(1)}& J_{AE}=\frac{\mathit{1}}{\mathit{2}N}{\displaystyle \sum }X-Y{}_{{}_{\mathit{2}}}{}^{{}^{\mathit{2}}}.\end{array}$$

Batch gradient descent is used to solve this optimization problem [19]. To prevent overfitting of the training data, a regularization term is added to the objective function to punish the overlearning of weights and parameters [20, 21], and the error function is$$\begin{array}{}\text{(2)}& J=J_{AE}+J_r,J_r=\frac{\lambda }{\mathit{2}}{\mathbf{W}}_{\mathrm{D}}{}_{{}_F}{}^{{}^{\mathit{2}}}+{\mathbf{W}}_E{}_{{}_F}{}^{{}^{\mathit{2}}}\text{,}\end{array}$$where ${}_F$is the Frobenius norm, and $\lambda$ is the coefficient of weight decay.

2.2. Stacked Autoencoder

When the features of standard autoencoders are cascaded, each autoencoder is used to continue to obtain the output of the new feature by the previous autoencoder, and a stacked autoencoder is obtained. When the number of stacked layers is large, direct training will encounter great difficulties. Generally, the method of training layer by layer is used to solve it. The first layer of the autoencoder is separated for independent training. After the first layer of self-encoder training is completed, the input of the second layer of an encoder is connected to the hidden layer output of the first layer of the self-encoder to form a new network for training. By analogy, the subsequent layers of autoencoders are sequentially added to the trained autoencoders network for training, until all network layers are added to complete the training. After the stacked autoencoder training is completed, the center hidden layer as the symmetry axis of the network architecture is used to connect the classifier. The features of the hidden layer are employed to distinguish the abnormal and normal samples.

2.3. Anomaly Detection with Stacked Autoencoder

The data acquired by the sensor is time series data. The sequence data collected by the sensor are segmented, and then, features are extracted for each segment. After the features extracted from the segmented data of multiple sensors are combined into one vector data, the vector data are input to the stacked autoencoder [17]. The final reconstruction error is calculated by stacking autoencoders. By analyzing the distribution of the reconstruction error of the normal detection data, the boundary threshold of the normal error can be determined, so that the reconstruction error can be used for anomaly detection. This anomaly detection scheme is illustrated in Figure 2.

[figure(s) omitted; refer to PDF]

Feature extraction from the time series of sensors is the key to data fusion and anomaly detection. In this article, the time-domain and frequency-domain characteristics of the time series are extracted and combined as the input of the autoencoder. The features calculated from the time-domain series include mean, standard deviation, and mean square root and denote sensor data series as x_i, the length of the series as N, and average as $\overline{x}$.$$\begin{array}{}\text{(3)}& f_{\mathit{1}}=\frac{\mathit{1}}{N}{\displaystyle \sum }_{i-\mathit{1}}^N{x}_i,f_{\mathit{2}}=\sqrt{\frac{\mathit{1}}{N}}{\displaystyle \sum }_{i=\mathit{1}}^N{x_i-\overline{x}}^{\mathit{2}},f_{\mathit{3}}=\sqrt{\frac{\mathit{1}}{N}}{\displaystyle \sum }_{i-\mathit{1}}^N{x}_i^{\mathit{2}}\text{.}\end{array}$$

Then, the segmented sensor data are decomposed into four layers with wavelet, and the following normalized power spectrum is also selected features$$\begin{array}{}\text{(7)}& f_{\mathit{4}}^k=\frac{{\displaystyle \sum }_{j=\mathit{1}}^M{P}_{kj}\mathit{1}n{P}_{kj}}{\mathit{1}nM}\text{,}\end{array}$$where M is the frequency components number and $P_{kj}$is the probability density of the jth frequency component of the kth decomposed subsignal, k = 0, 1, …, 15.

3. Experimental Results and Discussion

To obtain more accurate water quality detection results, the abnormal detection of coastal water quality uses the data of three sensors, which are pH value, conductivity, and ammonia nitrogen. pH value is measured with pH meter PHSJ-6L (Inesa Instrument, China). Online digital conductivity detection instrument KM-SAL-01 (KingMill Tech. Co, China) is employed for conductivity detection. Also, ammonia nitrogen is measured with the instrument HY-YDCG-Y01 (Haiyan Electronics, China).

Every sensor samples once every 20 minutes and 3 sampling data are obtained per hour. Considering the sequence characteristics of the data, anomaly detection is carried out in hours. The hourly data include the sampling data of the previous hour, the current hour, and the next hour. That is, the hourly data include 9 sampling values. Each sensor input of anomaly detection is 28 data composed of these 9 sampling values and 19 features obtained by the calculation method described in 2.2. The data of three sensors form a 28x3 matrix, which is used as the input of the autoencoder. After the autoencoder is trained, whether the output is abnormal by reconstructing the error.

There are 4,500 normal data and 300 abnormal data in the data set. Part of the abnormal data comes from actual data, and the other part comes from simulation data. The normal data and abnormal data in the data set are randomly selected according to 4:1 to determine the training sample set and the test sample set. The training sample set is used to train the autoencoder, and the test sample set is used to test with the trained autoencoder and compare performance indicators.

To compare different methods, three indicators are used to evaluate results, and they are recall, precision, accuracy, specificity, and F1 score [12, 13]. Among them, the F1 score is a comprehensive indicator that can more comprehensively reflect the performance of the method. When an anomaly is judged as an anomaly, it is marked as P; otherwise, it is marked as N. When this judgment is correct, it is marked as T; otherwise, it is marked as F. These indicators are calculated as follows:

(i) Recall = TP/(TP + FN)

(ii) Precision = TP/(TP + FP)

(iii) Accuracy = (TP + TN)/(TP + TN + FP + FN)

(iv) Specificity = TN/(TN + FP)

(v) F1-Score = 2PrecisionRecall/(Precision + Recall)

Two other commonly used anomaly detection methods are applied, and the results are used to evaluate detection performance. One is the one-class support vector machine (OCSVM) method [2]. Also, another one is the isolation forest (IF) method [7]. The training set is used to build the models of these methods, and the 900 normal data and 60 abnormal data in the test set are used to test these methods, and the results are listed in Table 2.

Table 2

Different method comparison for anomaly detection of seawater quality.

Method	Recall	Precision	Accuracy	Specificity	F1 score
One class SVM	0.783	0.746	0.970	0.982	0.764
Isolation forest	0.867	0.852	0.982	0.990	0.860
Proposed	1.000	0.938	0.996	0.996	0.968

Some anomaly examples are illustrated in Figure 3. A, B, and C are an anomaly, while D, E, and F are not an anomaly. Our proposed method provides correct results while the OCSVM method and IF method incorrectly mark D as an anomaly and OCSVM also marked E and F as anomalies. The judgment error of the two methods is due to insufficient use of all sensor information, and only the fluctuation of one sensor data is judged as abnormal. By observing the data and the figure, it is shown that the proposed method is the best one. The method proposed in this article surpasses the other two methods in every index. It has the best anomaly detection performance when it is compared with other methods and evaluated with all the indicators.

[figure(s) omitted; refer to PDF]

The proposed anomaly detection method is also applied to ship operation. Two sensor data are used, and they are the sailing angle and speed of the ship. The actual data of one-day sailing are used for anomaly detection with the proposed method. The anomaly detection results are shown in Figure 4.

[figure(s) omitted; refer to PDF]

The data in Figure 4 are compared and analyzed with the actual ship operating status. The ship suddenly found a moving float while cruising, so it carried out quick obstacle avoidance operations. It is manifested in the curve data that there are two rapid angle changes at high speed, and these changes correspond to two steering operations. Therefore, the anomaly detection results are correct.

This research is different from the existing water quality anomaly monitoring [1–7] in that anomaly detection is realized through the ship’s intelligent platform. This method can be integrated into the ship’s intelligent system, thereby providing more options for the automatic recording and analysis of anomaly detection results. For example, after detecting an abnormal seaside environment, the smart ship can automatically cruise and detect the area to locate the range of the abnormal area.

The real-time water quality detection method can solve the problem of full coverage of water quality monitoring in coastal waters so that data in the time and space dimensions can be obtained. The artificial intelligence method based on the autoencoder provided in this paper only uses normal data for learning and training. A large amount of normal data is easy to obtain, but it is very difficult to obtain abnormal data. Such methods are very important for anomaly detection. The method also shows advantages over existing methods in terms of performance such as accuracy.

4. Conclusions

An anomaly detection method is developed for coastal environment monitoring ships. Multisensor data fusion based on an autoencoder is employed for anomaly detection of seawater quality and ship operation. For the autoencoder that has completed the training, its calculation amount is not large when it is applied, and the calculation of the result can be completed within 1 second. Therefore, the proposed method in this paper is real time.

With the help of a smart ship platform and intelligent method, real-time monitoring and abnormal detection of the coastal environment are achieved. In future research, further factors affecting coastal water quality will be considered, such as coastal rainfall, river flow, surrounding factories, and residents’ activities.

With the application of real-time environmental detection based on USV, people can more completely monitor the quality and detect the anomaly of coastal water at different times and in different areas in time. On the one hand, the detection and response speed of water anomalies will be greatly improved. On the other hand, some seasonal or periodic abnormal changes will be found, and the sources and effects of these changes will be further studied. People will gain a deeper understanding of the relationship between the environment and human activities.

Acknowledgments

The publication of this research work is only for the academic purpose of Pentecost University, Accra, GHANA.

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