Naslov
Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study

Autori
Kalinić, Hrvoje ; Ćatipović, Leon ; Matić, Frano

Izvornik
Remote sensing (2072-4292) 14 (2022), 13; 2989, 16

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Interdisciplinarne prirodne znanosti, Računarstvo

POVEZANOST RADA