{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T14:39:52Z","timestamp":1772721592103,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the deposition of materials within the bed of the channel, which might lead to critical floods. A reliable monitoring system can thus help to protect properties and, in the most critical cases, save lives. A sensing system capable of monitoring the flow conditions and the possible geo-environmental constraints within a channel can operate using still images or video imaging. The latter approach better supports the above two features, but the acquisition of still images can display a better accuracy. To increase the accuracy of the video imaging approach, we propose an improved particle tracking algorithm for flow hydrodynamics supported by a machine learning approach based on a convolutional neural network-evolutionary fuzzy integral (CNN-EFI), with a sub-comparison performed by multi-layer perceptron (MLP). Both algorithms have been applied to process the video signals captured from a CMOS camera, which monitors the water flow of a channel that collects rain water from an upstream area to discharge it into the sea. The channel plays a key role in avoiding upstream floods that might pose a serious threat to the neighboring infrastructures and population. This combined approach displays reliable results in the field of environmental and hydrodynamic safety.<\/jats:p>","DOI":"10.3390\/s21124197","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T11:19:20Z","timestamp":1624015160000},"page":"4197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1762-419X","authenticated-orcid":false,"given":"Aim\u00e9","family":"Lay-Ekuakille","sequence":"first","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8159-9574","authenticated-orcid":false,"given":"John Djungha","family":"Okitadiowo","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Infrastructure and Sustainable Energy (DIIES), University \u201cMediterranean\u201d of Reggio Calabria, 89124 Reggio Calabria, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mo\u00efse","family":"Avoci Ugwiri","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Salerno, 84084 Fisciano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1523-6484","authenticated-orcid":false,"given":"Sabino","family":"Maggi","sequence":"additional","affiliation":[{"name":"CNR, National Research Council, Institute of Atmospheric Pollution Research, 70126 Bari, Italy"},{"name":"Faculty of Engineering, International Telematic University UniNettuno, 00186 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-0977","authenticated-orcid":false,"given":"Rita","family":"Masciale","sequence":"additional","affiliation":[{"name":"CNR, National Research Council, Water Research Institute, 70132 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5101-9826","authenticated-orcid":false,"given":"Giuseppe","family":"Passarella","sequence":"additional","affiliation":[{"name":"CNR, National Research Council, Water Research Institute, 70132 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1007\/s11069-020-03968-z","article-title":"Exceedance rate, exceedance probability, and the duality of GEV and GPD for extreme hazard analysis","volume":"102","author":"Huang","year":"2020","journal-title":"Nat. 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