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TECHNOLOGY AND CODE article

Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1533292

iMESc -A interactive machine learning app for environmental sciences

Provisionally accepted
Danilo Candido Vieira Danilo Candido Vieira 1*Fabiana S Paula Fabiana S Paula 1Luciana Yaginuma Luciana Yaginuma 2Gustavo Fonseca Gustavo Fonseca 1
  • 1 Federal University of São Paulo, São Paulo, Brazil
  • 2 Oceanographic Institute, University of São Paulo, São Paulo, São Paulo, Brazil

The final, formatted version of the article will be published soon.

    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.

    Keywords: Shiny1, Machine-Learning 2, Supervised3, Unsupervised4, Environmental Eciences5, Analytical Workflow6

    Received: 23 Nov 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Vieira, Paula, Yaginuma and Fonseca. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Danilo Candido Vieira, Federal University of São Paulo, São Paulo, Brazil

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.