Bayesian Machine Learning for Ecological and Environmental Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 96
Special Issue Editor
Interests: statistical modeling; probabilistic mapping; pattern recognition; machine learning; data science/analysis; detection, segmentation, and tracking optimization; signal/image/video processing; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Ecological and environmental systems are inherently complex, characterized by high-dimensional data, uncertainty, and intricate interdependencies. Bayesian machine learning (BML) provides a compelling framework for addressing these challenges by combining probabilistic reasoning, data-driven modeling, and prior knowledge to capture uncertainty and improve predictive performance. This Special Issue aims to highlight innovative applications of BML in ecology and environmental science, fostering the development of robust, interpretable, and scalable models to understand and manage natural systems.
We invite contributions that explore the theoretical advancements, methodological innovations, and practical applications of Bayesian machine learning in ecological and environmental contexts. Topics of interest include, but are not limited to, the following:
- Bayesian hierarchical models for species distribution and biodiversity analysis;
- Probabilistic modeling of ecological networks and interactions;
- Bayesian approaches to climate change that impact assessment and environmental risk analysis;
- Data assimilation and state-space models for ecosystem dynamics;
- The quantification of uncertainty in environmental predictions and decision-making;
- Spatial and spatiotemporal modeling using Bayesian techniques;
- Bayesian nonparametric methods for ecological data analysis;
- Integration of remote sensing data with Bayesian machine learning;
- Case studies demonstrating successful BML applications in conservation and environmental management.
Through this Special Issue, we seek to advance the role of Bayesian machine learning in tackling pressing ecological and environmental challenges. We encourage interdisciplinary contributions from researchers and practitioners in fields such as ecology, environmental science, statistics, machine learning, and data science.
Dr. Nezamoddin N. Kachouie
Guest Editor
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. Algorithms is an international peer-reviewed open access monthly 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
- Bayesian machine learning
- ecological modeling
- environmental applications
- probabilistic inference
- uncertainty quantification
- spatial modeling
- biodiversity analysis
- climate impact assessment
- data assimilation
- conservation science
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