iMESc – an interactive machine learning app for environmental sciences
As environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. However, the integration of ML into workflows can pose challenges due to technical barriers or the time-intensive nature of coding. To address these i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1533292/full |
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| author | Danilo Cândido Vieira Danilo Cândido Vieira Fabiana S. Paula Luciana Erika Yaginuma Gustavo Fonseca |
| author_facet | Danilo Cândido Vieira Danilo Cândido Vieira Fabiana S. Paula Luciana Erika Yaginuma Gustavo Fonseca |
| author_sort | Danilo Cândido Vieira |
| collection | DOAJ |
| description | As environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. However, the integration of ML into workflows can pose challenges due to technical barriers or the time-intensive nature of coding. To address these issues, we developed iMESc, an interactive ML app designed to streamline and simplify ML workflows for environmental data. Developed in R and built on the Shiny platform, iMESc enables the integration of supervised and unsupervised ML methods, along with tools for data preprocessing, visualization, descriptive statistics, and spatial analysis. The Datalist system ensures seamless transitions between analytical workflows, while the “savepoints” feature enhances reproducibility by preserving the analysis state. We demonstrate iMESc’s flexibility with four workflows applied to a case study predicting nematode community structure based on environmental data. The classical statistical approaches, the Redundancy Analysis (RDA) and Piecewise RDA (pwRDA), explained 30.7% and 53%, respectively. The SuperSOM model achieved an R2 of 0.60 for training and 0.291 for testing, identifying spatial patterns across depth zones. Finally, a hybrid model combining an unsupervised SOM and followed by the supervised Random Forest model returned an accuracy of 83.47% for the training and 80.77% for the test, with Bathymetry, Chlorophyll, and Coarse Sand as key predictive variables. IMESc permits the customization of plots and saving the workflows into “savepoints” guarantying reproducibility. iMESc bridges the gap between the complexity of machine learning algorithms and the need for user-friendly interfaces in environmental research. By reducing the technical burden of coding, iMESc allows researchers to focus on scientific inquiry, improving both the efficiency and depth of their analyses. |
| format | Article |
| id | doaj-art-44e1cc5e9b9e44fd9459fb1bcc25d3bf |
| institution | Kabale University |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-44e1cc5e9b9e44fd9459fb1bcc25d3bf2025-01-31T06:39:55ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-01-011310.3389/fenvs.2025.15332921533292iMESc – an interactive machine learning app for environmental sciencesDanilo Cândido Vieira0Danilo Cândido Vieira1Fabiana S. Paula2Luciana Erika Yaginuma3Gustavo Fonseca4Instituto do Mar, Campus Baixada Santista, Universidade Federal de São Paulo, Santos, BrazilInstituto Oceanográfico, Universidade de São Paulo, São Paulo, BrazilInstituto do Mar, Campus Baixada Santista, Universidade Federal de São Paulo, Santos, BrazilInstituto Oceanográfico, Universidade de São Paulo, São Paulo, BrazilInstituto do Mar, Campus Baixada Santista, Universidade Federal de São Paulo, Santos, BrazilAs environmental sciences increasingly rely on complex datasets, machine learning (ML) has become crucial for identifying patterns and relationships. However, the integration of ML into workflows can pose challenges due to technical barriers or the time-intensive nature of coding. To address these issues, we developed iMESc, an interactive ML app designed to streamline and simplify ML workflows for environmental data. Developed in R and built on the Shiny platform, iMESc enables the integration of supervised and unsupervised ML methods, along with tools for data preprocessing, visualization, descriptive statistics, and spatial analysis. The Datalist system ensures seamless transitions between analytical workflows, while the “savepoints” feature enhances reproducibility by preserving the analysis state. We demonstrate iMESc’s flexibility with four workflows applied to a case study predicting nematode community structure based on environmental data. The classical statistical approaches, the Redundancy Analysis (RDA) and Piecewise RDA (pwRDA), explained 30.7% and 53%, respectively. The SuperSOM model achieved an R2 of 0.60 for training and 0.291 for testing, identifying spatial patterns across depth zones. Finally, a hybrid model combining an unsupervised SOM and followed by the supervised Random Forest model returned an accuracy of 83.47% for the training and 80.77% for the test, with Bathymetry, Chlorophyll, and Coarse Sand as key predictive variables. IMESc permits the customization of plots and saving the workflows into “savepoints” guarantying reproducibility. iMESc bridges the gap between the complexity of machine learning algorithms and the need for user-friendly interfaces in environmental research. By reducing the technical burden of coding, iMESc allows researchers to focus on scientific inquiry, improving both the efficiency and depth of their analyses.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1533292/fullshinymachine-learningsupervisedunsupervisedenvironmental sciencesanalytical workflow |
| spellingShingle | Danilo Cândido Vieira Danilo Cândido Vieira Fabiana S. Paula Luciana Erika Yaginuma Gustavo Fonseca iMESc – an interactive machine learning app for environmental sciences Frontiers in Environmental Science shiny machine-learning supervised unsupervised environmental sciences analytical workflow |
| title | iMESc – an interactive machine learning app for environmental sciences |
| title_full | iMESc – an interactive machine learning app for environmental sciences |
| title_fullStr | iMESc – an interactive machine learning app for environmental sciences |
| title_full_unstemmed | iMESc – an interactive machine learning app for environmental sciences |
| title_short | iMESc – an interactive machine learning app for environmental sciences |
| title_sort | imesc an interactive machine learning app for environmental sciences |
| topic | shiny machine-learning supervised unsupervised environmental sciences analytical workflow |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1533292/full |
| work_keys_str_mv | AT danilocandidovieira imescaninteractivemachinelearningappforenvironmentalsciences AT danilocandidovieira imescaninteractivemachinelearningappforenvironmentalsciences AT fabianaspaula imescaninteractivemachinelearningappforenvironmentalsciences AT lucianaerikayaginuma imescaninteractivemachinelearningappforenvironmentalsciences AT gustavofonseca imescaninteractivemachinelearningappforenvironmentalsciences |