DeepData: Machine learning in the marine ecosystems. (15th November 2022) - Oliveira e Silva, Leonor; Resende, Magda; Galhardas, Helena; Manquinho, Vasco; Lynce, Inês
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 allows the user to insert its own species and environmental data.