Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean)
Algorithms based on a clever exploitation of artificial intelligence (AI) techniques are the key for modern multidisciplinary applications that are being developed in the last decades. AI approaches’ ability of extracting relevant information from data is essential to perform comprehensive studies i...
Published in: | Multimodal Sensing: Technologies and Applications |
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E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller
2019
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Online Access: | http://hdl.handle.net/11563/139712 https://doi.org/10.1117/12.2527534 |
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ftunivbasilicata:oai:iris.unibas.it:11563/139712 2024-04-14T08:20:07+00:00 Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) Renò V. Fanizza C. Dimauro G. Telesca V. Dibari P. Gala G. Mosca N. Cipriano G. Carlucci R Maglietta R. E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller Renò, V. Fanizza, C. Dimauro, G. Telesca, V. Dibari, P. Gala, G. Mosca, N. Cipriano, G. Carlucci, R Maglietta, R. 2019 http://hdl.handle.net/11563/139712 https://doi.org/10.1117/12.2527534 eng eng E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller country:USA place:Bellingham, WA info:eu-repo/semantics/altIdentifier/isbn/978-151062797-0 ispartofbook:Proceedings of SPIE - The International Society for Optical Engineering 2019 Multimodal Sensing: Technologies and Applications 2019; Munich; Germany; 26 June 2019 through 27 June 2019; Code 151670 volume:11059 alleditors:E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller http://hdl.handle.net/11563/139712 doi:10.1117/12.2527534 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85072647344 Whales Whale Sperm whale info:eu-repo/semantics/bookPart 2019 ftunivbasilicata https://doi.org/10.1117/12.2527534 2024-03-21T17:32:08Z Algorithms based on a clever exploitation of artificial intelligence (AI) techniques are the key for modern multidisciplinary applications that are being developed in the last decades. AI approaches’ ability of extracting relevant information from data is essential to perform comprehensive studies in new multidisciplinary topics such as ecological informatics. For example, improving knowledge on cetaceans’ distribution patterns enables the acquisition of a strategic expertise for developing tools aimed to the preservation of the marine environment. In this paper we present an innovative approach, based on Random Forest and RUSBoost, aimed to define predictive models for presence/absence and abundance estimation of two classes of cetaceans: the striped dolphin Stenella coeruleoalba and the common bottlenose dolphin Tursiops truncatus. Sightings data from 2009 to 2017 have been collected and enriched by geo-morphological and meteorological data in order to build a comprehensive dataset of real observations used to train and validate the proposed algorithms. Results in terms of classification and regression accuracy demonstrate the feasibility of the proposed approach and suggest the application of such artificial intelligence based techniques to larger datasets, with the aim of enabling large scale studies as well as improving knowledge on data deficient species. Book Part Sperm whale Università degli Studi della Basilicata: CINECA IRIS Multimodal Sensing: Technologies and Applications 42 |
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Open Polar |
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Università degli Studi della Basilicata: CINECA IRIS |
op_collection_id |
ftunivbasilicata |
language |
English |
topic |
Whales Whale Sperm whale |
spellingShingle |
Whales Whale Sperm whale Renò V. Fanizza C. Dimauro G. Telesca V. Dibari P. Gala G. Mosca N. Cipriano G. Carlucci R Maglietta R. Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) |
topic_facet |
Whales Whale Sperm whale |
description |
Algorithms based on a clever exploitation of artificial intelligence (AI) techniques are the key for modern multidisciplinary applications that are being developed in the last decades. AI approaches’ ability of extracting relevant information from data is essential to perform comprehensive studies in new multidisciplinary topics such as ecological informatics. For example, improving knowledge on cetaceans’ distribution patterns enables the acquisition of a strategic expertise for developing tools aimed to the preservation of the marine environment. In this paper we present an innovative approach, based on Random Forest and RUSBoost, aimed to define predictive models for presence/absence and abundance estimation of two classes of cetaceans: the striped dolphin Stenella coeruleoalba and the common bottlenose dolphin Tursiops truncatus. Sightings data from 2009 to 2017 have been collected and enriched by geo-morphological and meteorological data in order to build a comprehensive dataset of real observations used to train and validate the proposed algorithms. Results in terms of classification and regression accuracy demonstrate the feasibility of the proposed approach and suggest the application of such artificial intelligence based techniques to larger datasets, with the aim of enabling large scale studies as well as improving knowledge on data deficient species. |
author2 |
E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller Renò, V. Fanizza, C. Dimauro, G. Telesca, V. Dibari, P. Gala, G. Mosca, N. Cipriano, G. Carlucci, R Maglietta, R. |
format |
Book Part |
author |
Renò V. Fanizza C. Dimauro G. Telesca V. Dibari P. Gala G. Mosca N. Cipriano G. Carlucci R Maglietta R. |
author_facet |
Renò V. Fanizza C. Dimauro G. Telesca V. Dibari P. Gala G. Mosca N. Cipriano G. Carlucci R Maglietta R. |
author_sort |
Renò V. |
title |
Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) |
title_short |
Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) |
title_full |
Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) |
title_fullStr |
Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) |
title_full_unstemmed |
Predictive models for abundance estimation and distribution maps of the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus in the Northern Ionian Sea (North-eastern Central Mediterranean) |
title_sort |
predictive models for abundance estimation and distribution maps of the striped dolphin stenella coeruleoalba and the bottlenose dolphin tursiops truncatus in the northern ionian sea (north-eastern central mediterranean) |
publisher |
E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller |
publishDate |
2019 |
url |
http://hdl.handle.net/11563/139712 https://doi.org/10.1117/12.2527534 |
genre |
Sperm whale |
genre_facet |
Sperm whale |
op_relation |
info:eu-repo/semantics/altIdentifier/isbn/978-151062797-0 ispartofbook:Proceedings of SPIE - The International Society for Optical Engineering 2019 Multimodal Sensing: Technologies and Applications 2019; Munich; Germany; 26 June 2019 through 27 June 2019; Code 151670 volume:11059 alleditors:E. Stella, S. Negahdaripour, D. Ceglarek, C. Moller http://hdl.handle.net/11563/139712 doi:10.1117/12.2527534 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85072647344 |
op_doi |
https://doi.org/10.1117/12.2527534 |
container_title |
Multimodal Sensing: Technologies and Applications |
container_start_page |
42 |
_version_ |
1796298330691600384 |