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Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study (CROSBI ID 311421)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Kalinić, Hrvoje ; Ćatipović, Leon ; Matić, Frano Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study // Remote sensing, 14 (2022), 13; 2989, 16. doi: 10.3390/rs14132989

Podaci o odgovornosti

Kalinić, Hrvoje ; Ćatipović, Leon ; Matić, Frano

engleski

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 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.

optimal sensor placement ; feature selection ; unsupervised learning ; clustering ; self-organizing maps ; neural gas ; k-means

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Podaci o izdanju

14 (13)

2022.

2989

16

objavljeno

2072-4292

10.3390/rs14132989

Povezanost rada

Interdisciplinarne prirodne znanosti, Računarstvo

Poveznice
Indeksiranost