{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:42:12Z","timestamp":1760229732067,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004488","name":"Croatian Science Foundation","doi-asserted-by":"publisher","award":["UIP-2019-04-1737"],"award-info":[{"award-number":["UIP-2019-04-1737"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs14132989","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T23:11:19Z","timestamp":1655939479000},"page":"2989","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimal Sensor Placement Using Learning Models\u2014A Mediterranean Case Study"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8567-851X","authenticated-orcid":false,"given":"Hrvoje","family":"Kalini\u0107","sequence":"first","affiliation":[{"name":"Department of Informatics, Faculty of Science, University of Split, 21000 Split, Croatia"}]},{"given":"Leon","family":"\u0106atipovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Physics, Faculty of Science, University of Split, 21000 Split, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0392-4172","authenticated-orcid":false,"given":"Frano","family":"Mati\u0107","sequence":"additional","affiliation":[{"name":"Physical Oceanography Laboratory, Institute of Oceanography and Fisheries, 21000 Split, Croatia"},{"name":"University Department of Marine Studies, University of Split, 21000 Split, Croatia"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"ref_1","unstructured":"Jaimes, A., Tweedie, C., Mago\u010d, T., Kreinovich, V., and Ceberio, M. 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