A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment
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 co...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4632137 |
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| author | Chong Qu Zhiguo Zhou Zhiwen Liu Shuli Jia Liyong Ma Mary Immaculate Sheela L |
| author_facet | Chong Qu Zhiguo Zhou Zhiwen Liu Shuli Jia Liyong Ma Mary Immaculate Sheela L |
| author_sort | Chong Qu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eea7e3619a6b406ca3bc936b125a135a |
| institution | DOAJ |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-eea7e3619a6b406ca3bc936b125a135a2025-08-20T03:21:13ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/4632137A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal EnvironmentChong Qu0Zhiguo Zhou1Zhiwen Liu2Shuli Jia3Liyong Ma4Mary Immaculate Sheela L5School of Information and ElectronicsSchool of Information and ElectronicsSchool of Information and ElectronicsAutomation Engineering DepartmentSchool of Science and EngineeringDEAN-FESACGreat 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.http://dx.doi.org/10.1155/2022/4632137 |
| spellingShingle | Chong Qu Zhiguo Zhou Zhiwen Liu Shuli Jia Liyong Ma Mary Immaculate Sheela L A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment Advances in Materials Science and Engineering |
| title | A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment |
| title_full | A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment |
| title_fullStr | A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment |
| title_full_unstemmed | A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment |
| title_short | A Multisensor Data Fusion Based Anomaly Detection (Ammonia Nitrogen) Approach for Ensuring Green Coastal Environment |
| title_sort | multisensor data fusion based anomaly detection ammonia nitrogen approach for ensuring green coastal environment |
| url | http://dx.doi.org/10.1155/2022/4632137 |
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