DeepData: Machine learning in the marine ecosystems. (15th November 2022)
- Record Type:
- Journal Article
- Title:
- DeepData: Machine learning in the marine ecosystems. (15th November 2022)
- Main Title:
- DeepData: Machine learning in the marine ecosystems
- Authors:
- Oliveira e Silva, Leonor
Resende, Magda
Galhardas, Helena
Manquinho, Vasco
Lynce, Inês - Abstract:
- 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. Highlights: DeepData is a species distribution model web-based tool for model simulation. DeepData allows the use of various statistical and machine learning models. DeepData models' parameters can be specified by the user. DeepData allowsAbstract: 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. Highlights: DeepData is a species distribution model web-based tool for model simulation. DeepData allows the use of various statistical and machine learning models. DeepData models' parameters can be specified by the user. DeepData allows the user to insert its own species and environmental data. … (more)
- Is Part Of:
- Expert systems with applications. Volume 206(2022)
- Journal:
- Expert systems with applications
- Issue:
- Volume 206(2022)
- Issue Display:
- Volume 206, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 206
- Issue:
- 2022
- Issue Sort Value:
- 2022-0206-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-11-15
- Subjects:
- Machine learning -- Species distribution models -- Marine ecosystems
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2022.117841 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23554.xml