Document Type
Article
Publication Date
2021
Keywords
Deep learning, Ecological prediction, Scalability, Sequential data, Temporal ecology, Time series
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.ecoinf.2021.101252
Abstract
Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.
Rights Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Ecological Informatics, v. 61, art. 101252
Scholar Commons Citation
Capinha, César; Ceia-Hasse, Ana; Kramer, Andrew M.; and Meijer, Christiaan, "Deep Learning for Supervised Classification of Temporal Data in Ecology" (2021). Integrative Biology Faculty and Staff Publications. 537.
https://digitalcommons.usf.edu/bin_facpub/537
