Special Issue "Machine Learning in Soil and Environmental Science"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Machine Learning".

Deadline for manuscript submissions: 30 April 2023 | Viewed by 1529

Special Issue Editors

Prof. Dr. Hossein Bonakdari
E-Mail Website
Guest Editor
Department of Soil and Agri-Food Engineering, Laval University, Québec, QC G1V 0A6, Canada
Interests: machine learning; sustainable development; water management; irrigation; artificial intelligence; data processing
Prof. Dr. Taha Ouarda
E-Mail Website
Guest Editor
Institut national de la recherche scientifique (INRS-ETE), Québec, QC G1K 9A9, Canada
Interests: hydrology; climatology; statistics; environmental modeling
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Special Issue Information

Dear Colleagues,

Accurate analysis and modeling of environmental problems is a challenging task due to the large number of variables related to environmental outcomes. In addition, the randomness inherent of these variables produces intricate nonlinear data patterns, which cannot be properly addressed by conventional methods. Therefore, there is an urgent need to assess, solve, improve, and optimize practical environmental and engineering problems through data analysis methods and artificial intelligence methods, in order for decision-makers to accurately assess vulnerable areas and formulate future management/adaptation strategies. Profound contemporary advances in environmental sciences have taken place around us, supported by remarkable enhancements in machine learning techniques. These approaches have been employed predominantly as a promising tool to resolve environmental problems to extract and predict patterns from complex data.

This Special Issue covers a wide variety of subjects for applications of machine learning techniques in environmental problems, and aims to be a forum for researchers to publish their recent studies into this crucial area by focusing on: (i) proposing more accurate and reliable modeling methodologies; (ii) improving the accuracy of conventional methods and/or environmental impact assessments; and (iii) establishing a knowledge-based decision support tool through integrated machine leaning-based answers to both quantitative and qualitative environmental issues. Relevant topics include, but are not limited to, the following areas:

  • Water resource management;
  • Environmental and biological conservation;
  • Remote sensing and geographic information systems;
  • Pollution prevention and remediation;
  • Energy and climate change;
  • Agroforestry;
  • Solar system;
  • Aquatic/marine biology;
  • Soil ecology;
  • Earth systems.

Prof. Dr. Hossein Bonakdari
Prof. Dr. Taha Ouarda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water
  • soil
  • atmosphere
  • ocean
  • land surface
  • space
  • solar system
  • soft computing
  • modeling
  • development

Published Papers (1 paper)

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Research

Article
Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications
Informatics 2021, 8(3), 58; https://doi.org/10.3390/informatics8030058 - 03 Sep 2021
Cited by 2 | Viewed by 993
Abstract
In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred [...] Read more.
In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6). Full article
(This article belongs to the Special Issue Machine Learning in Soil and Environmental Science)
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