Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study
In this paper, we discuss different approaches to optimal sensor placement and propose that an optimal sensor location can be selected using unsupervised learning methods such as self-organising maps, neural gas or the K-means algorithm. We show how each of the algorithms can be used for this purpos...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/13/2989 |
| _version_ | 1797408327065403392 |
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| author | Hrvoje Kalinić Leon Ćatipović Frano Matić |
| author_facet | Hrvoje Kalinić Leon Ćatipović Frano Matić |
| author_sort | Hrvoje Kalinić |
| collection | DOAJ |
| description | In this paper, we discuss different approaches to optimal sensor placement and propose that an optimal sensor location can be selected using unsupervised learning methods such as self-organising maps, neural gas or the K-means algorithm. We show how each of the algorithms can be used for this purpose and that additional constraints such as distance from shore, which is presumed to be related to deployment and maintenance costs, can be considered. The study uses wind data over the Mediterranean Sea and uses the reconstruction error to evaluate sensor location selection. The reconstruction error shows that results deteriorate when additional constraints are added to the equation. However, it is also shown that a small fraction of the data is sufficient to reconstruct wind data over a larger geographic area with an error comparable to that of a meteorological model. The results are confirmed by several experiments and are consistent with the results of previous studies. |
| first_indexed | 2024-03-09T03:56:50Z |
| format | Article |
| id | doaj.art-b90b1656dac64ee9be30f6e3611e7da2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:56:50Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b90b1656dac64ee9be30f6e3611e7da22023-12-03T14:19:43ZengMDPI AGRemote Sensing2072-42922022-06-011413298910.3390/rs14132989Optimal Sensor Placement Using Learning Models—A Mediterranean Case StudyHrvoje Kalinić0Leon Ćatipović1Frano Matić2Department of Informatics, Faculty of Science, University of Split, 21000 Split, CroatiaDepartment of Physics, Faculty of Science, University of Split, 21000 Split, CroatiaPhysical Oceanography Laboratory, Institute of Oceanography and Fisheries, 21000 Split, CroatiaIn this paper, we discuss different approaches to optimal sensor placement and propose that an optimal sensor location can be selected using unsupervised learning methods such as self-organising maps, neural gas or the K-means algorithm. We show how each of the algorithms can be used for this purpose and that additional constraints such as distance from shore, which is presumed to be related to deployment and maintenance costs, can be considered. The study uses wind data over the Mediterranean Sea and uses the reconstruction error to evaluate sensor location selection. The reconstruction error shows that results deteriorate when additional constraints are added to the equation. However, it is also shown that a small fraction of the data is sufficient to reconstruct wind data over a larger geographic area with an error comparable to that of a meteorological model. The results are confirmed by several experiments and are consistent with the results of previous studies.https://www.mdpi.com/2072-4292/14/13/2989optimal sensor placementfeature selectionunsupervised learningclusteringself-organizing mapsneural gas |
| spellingShingle | Hrvoje Kalinić Leon Ćatipović Frano Matić Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study Remote Sensing optimal sensor placement feature selection unsupervised learning clustering self-organizing maps neural gas |
| title | Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study |
| title_full | Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study |
| title_fullStr | Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study |
| title_full_unstemmed | Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study |
| title_short | Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study |
| title_sort | optimal sensor placement using learning models a mediterranean case study |
| topic | optimal sensor placement feature selection unsupervised learning clustering self-organizing maps neural gas |
| url | https://www.mdpi.com/2072-4292/14/13/2989 |
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