Document of bibliographic reference 353713

BibliographicReference record

Type

Bibliographic resource

Type of document

Journal article

BibLvlCode

AS

Title

DeepData: Machine learning in the marine ecosystems

Abstract

Based on environmental and species monitoring data, Species Distribution Modelling (SDM) tries to build a model to predict the distribution of a species across a geographic area. These models can then be used to manage the activities in the area in order to prevent negative economic and environmental impacts. In marine ecosystems, SDM can be used to regulate fishing practices or manage protected areas.This paper presents DeepData, a new no-code web-based machine learning platform to facilitate the work of marine biologists with SDM. The DeepData tool enables to automate SDM, by automating the creation and validation of the model by marine biologists. Biologists mostly use probabilistic algorithms, such as maximum entropy, generalized linear models and generalized additive models. The DeepData tool also allows the use of machine learning algorithms, such as classification and regression trees, random forests and support vector machines. Moreover, besides the usage of machine learning algorithms, other steps in SDM, such as data preparation and model evaluation, are also discussed in the paper. Furthermore, a concrete explanation of the use of the DeepData tool is presented, as well as the details of implementation and evaluation.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000819427000001

Bibliographic citation

Oliveira e Silva, L.; Resende, M.; Galhardas, H.; Manquinho, V.; Lynce, I. (2022). DeepData: Machine learning in the marine ecosystems. Exp. Syst. Appl. 206: 117841. https://dx.doi.org/10.1016/j.eswa.2022.117841

Topic

Marine

Is peer reviewed

true

Access rights

open access

Is accessible for free

true