Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables
Abstract
(ISSN 2766-6190)
Advances in Environmental and Engineering Research (AEER) is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. This periodical is devoted to publishing high-quality peer-reviewed papers that describe the most significant and cutting-edge research in all areas of environmental science and engineering. Work at any scale, from molecular biology to ecology, is welcomed.
Main research areas include (but are not limited to):
Advances in Environmental and Engineering Research publishes a variety of article types (Original Research, Review, Communication, Opinion, Comment, Conference Report, Technical Note, Book Review, etc.). We encourage authors to be succinct; however, authors should present their results in as much detail as necessary. Reviewers are expected to emphasize scientific rigor and reproducibility.
Publication Speed (median values for papers published in 2023): Submission to First Decision: 6.1 weeks; Submission to Acceptance: 16.1 weeks; Acceptance to Publication: 9 days (1-2 days of FREE language polishing included)
Special Issue
Artificial Intelligence in Environmental Research
Submission Deadline: July 30, 2024 (Open) Submit Now
Guest Editors
José L. Segovia-Juárez, PhD
National University of Engineering, Peru. (Universidad Nacional de Ingenieria, Lima, Peru)
Research Interests: Applications of Artificial Intelligence in biomedical problems; Simulation of complex systems
Cosimo Magazzino, PhD
Department of Political Sciences, Roma Tre University, Via G. Chiabrera 199, 00145, Rome (RM), Italy
Research Interests: Public finance; Energy econometrics; Environmental Kuznets curve; Time series econometrics; Panel data models; Machine Learning experiments; Artificial Neural Networks analysis; Relationship among CO2 emissions, GDP and energy consumption
About This Topic
Welcome to the Special Issue of Advances in Environmental and Engineering Research on "Artificial Intelligence in Environmental Research".
In recent years, the intersection of artificial intelligence (AI) and environmental research has emerged as a pivotal area of study, offering innovative solutions to address pressing environmental challenges. From climate change mitigation to biodiversity conservation, AI technologies have demonstrated remarkable potential to revolutionize how we understand, monitor, and manage our natural surroundings.
This special issue aims to explore the multifaceted applications of AI in environmental research, showcasing cutting-edge studies, methodologies, and advancements in this rapidly evolving field. By bringing together researchers, practitioners, and experts from diverse disciplines, we seek to foster a deeper understanding of the role of AI in enhancing our stewardship of the environment.
Through a collection of original research articles, reviews, and case studies, this issue will delve into various aspects of AI in environmental research, including but not limited to:
a. Remote Sensing and Earth Observation: Leveraging AI techniques for satellite imagery analysis, land cover classification, and monitoring environmental changes at local and global scales.
b. Climate Modeling and Prediction: Utilizing AI-driven models to enhance climate forecasting, assess climate impacts, and develop strategies for climate adaptation and resilience.
c. Biodiversity Monitoring and Conservation: Applying AI algorithms for species identification, habitat mapping, and predicting species distribution patterns to inform conservation efforts.
d. Natural Resource Management: Harnessing AI-powered tools for optimizing resource allocation, sustainable land use planning, and water resource management.
e. Pollution Monitoring and Control: Implementing AI-based systems for real-time pollution detection, air and water quality monitoring, and remediation strategies.
f. Ecological Restoration and Ecosystem Services: Using AI techniques to support ecological restoration projects, quantify ecosystem services, and promote biodiversity conservation.
This special issue aims to advance the frontier of environmental research by showcasing the transformative potential of AI technologies, including LLMs, in addressing complex environmental issues. We invite researchers and practitioners to contribute their original research and insights to this interdisciplinary dialogue, ultimately driving forward our collective efforts towards a more sustainable and resilient planet.
Keywords
Artificial Intelligence
Large Language Models
Environmental Research
Remote Sensing
Climate Modeling
Biodiversity Conservation
Natural Resource Management
Pollution Monitoring
Ecological Restoration
Sustainability
Conservation Technology
Manuscript Submission Information
Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office (aeer@lidsen.com) for record. If you have any questions, please do not hesitate to contact the Editorial Office.
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Publication
Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variablesby
Fuad Bin Nasir
and
Jin Li
Abstract Increasing urban wastewater and rigorous discharge regulations pose significant challenges for wastewater treatment plants (WWTP) to meet regulatory compliance while minimizing operational costs. This study explores the application of several machine learning (ML) models specifically, Artificial Neural Networks (ANN), Gradient Boosting Machine [...] |
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