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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-07, Vol.14 (13), p.2989
Main Authors: Kalinić, Hrvoje, Ćatipović, Leon, Matić, Frano
Format: Article
Language:English
Subjects:
Algorithms
Climate change
Clustering
Feature selection
Machine learning
Maintenance costs
neural gas
optimal sensor placement
Reconstruction
self-organizing maps
Sensors
Site selection
unsupervised learning
Wind
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14132989