Logbook data constitute a key element within the electronic recording and reporting system of the Euro- pean Fisheries Control Technologies Framework and are used to record, report, process, store and send information about fishing operations, including landings and fishing gear. A relevant application of log- book data is to account for the heterogeneity of fishing practices (e.g., by gear or métier), which is a key aspect of the Common Fishery Policy. However, despite their importance, few published studies have explored the potential and pitfalls of logbook data, even in combination with other powerful data sources such as the Vessel Monitoring System (VMS). Here, a new approach to characterizing the com- position of landings for the different types of gear based on the use of Self-Organizing Maps (SOMs − a particular type of Artificial Neural Network) is applied to the Italian fleet logbook dataset. The SOM is trained on the landings composition and the resulting patterns are interpreted using some measures obtained from the analysis of the corresponding VMS data. Namely, the mean sea bottom depth and the area of activity are obtained for each fishing trip. Moreover, the ability of the trained SOM to predict gear from landings is tested using a new dataset. The trained SOM classifies logbook records according to the ecological, taxonomical, and trophic characteristics of the species caught, and the depth of fishing activities plays an important role in diversifying the landings associated with certain widely used fishing gear such as the bottom otter trawl. The clustering of SOM units allows the identification of a set of 12 groups, which are strongly related to the types of gear used by the Italian fleet. Furthermore, the trained SOM shows a high ability to recognize gear from logbook data, thus confirming the robustness of the landings profiles detected.

Russo, T., Carpentieri, P., Fiorentino, F., Arneri, E., Scardi, M., Cioffi, A., et al. (2016). Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data. FISHERIES RESEARCH, 181, 34-47 [10.1016/j.fishres.2016.04.005].

Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data

RUSSO, TOMMASO;SCARDI, MICHELE;CATAUDELLA, STEFANO
2016

Abstract

Logbook data constitute a key element within the electronic recording and reporting system of the Euro- pean Fisheries Control Technologies Framework and are used to record, report, process, store and send information about fishing operations, including landings and fishing gear. A relevant application of log- book data is to account for the heterogeneity of fishing practices (e.g., by gear or métier), which is a key aspect of the Common Fishery Policy. However, despite their importance, few published studies have explored the potential and pitfalls of logbook data, even in combination with other powerful data sources such as the Vessel Monitoring System (VMS). Here, a new approach to characterizing the com- position of landings for the different types of gear based on the use of Self-Organizing Maps (SOMs − a particular type of Artificial Neural Network) is applied to the Italian fleet logbook dataset. The SOM is trained on the landings composition and the resulting patterns are interpreted using some measures obtained from the analysis of the corresponding VMS data. Namely, the mean sea bottom depth and the area of activity are obtained for each fishing trip. Moreover, the ability of the trained SOM to predict gear from landings is tested using a new dataset. The trained SOM classifies logbook records according to the ecological, taxonomical, and trophic characteristics of the species caught, and the depth of fishing activities plays an important role in diversifying the landings associated with certain widely used fishing gear such as the bottom otter trawl. The clustering of SOM units allows the identification of a set of 12 groups, which are strongly related to the types of gear used by the Italian fleet. Furthermore, the trained SOM shows a high ability to recognize gear from logbook data, thus confirming the robustness of the landings profiles detected.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore BIO/07
English
VMS Métier Self-Organizing Maps Neural networks
http://www.sciencedirect.com/science/article/pii/S016578361630100X
Russo, T., Carpentieri, P., Fiorentino, F., Arneri, E., Scardi, M., Cioffi, A., et al. (2016). Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data. FISHERIES RESEARCH, 181, 34-47 [10.1016/j.fishres.2016.04.005].
Russo, T; Carpentieri, P; Fiorentino, F; Arneri, E; Scardi, M; Cioffi, A; Cataudella, S
Articolo su rivista
File in questo prodotto:
File Dimensione Formato  
2016-Russo_et_al. - Fisheries Research.pdf

non disponibili

Licenza: Copyright dell'editore
Dimensione 5.58 MB
Formato Adobe PDF
5.58 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/169037
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 25
  • ???jsp.display-item.citation.isi??? 20
social impact