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Application of Artificial Intelligence in Environmental Science and Engineering

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 5149

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Special Issue Editors

Dr. Zhenxing Wang

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Guest Editor

South China Institute of Environmental Sciences, Ministry of Ecology and Environment of the People’s Republic of China, Guangzhou 510655, China
Interests: artificial intelligence; environmental systems modelling; contaminant transport and removal modelling
Special Issues, Collections and Topics in MDPI journals

Dr. Kallol Roy

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Guest Editor

Institute of Computer Science, Faculty of Science and Technology, University of Tartu, 51009 Tartu, Estonia
Interests: deep learning; semi-supervised learning; ethical AI; AI accelerator; computer vision; non-convex optimization; electromagnetics

Dr. Cheng Gao

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Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: urban water; wate quality; hydrology; hydrodynamic model; river basin management

Dr. Lei Huang

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Guest Editor

School of Environmental Science and Engineering, Guangzhou University, Guangzhou 510006, China
Interests: density functional calculation; fluoride; artificial intelligence data-driven models; optimization of chemical functional materials
Special Issues, Collections and Topics in MDPI journals

Dr. Yunjun Yu

E-Mail Website
Guest Editor

South China Institute of Environmental Sciences, Ministry of Ecology and Environment of the People’s Republic of China, Guangzhou 510655, China
Interests: artificial intelligence; marine pollution prediction; marine environment modelling

Special Issue Information

Dear Colleagues,

Driven by the rapid development in information technology and intelligence, artificial intelligence (AI), a major frontier technology, is widely used in a variety of fields. Research has found that AI in environmental science and engineering has great potential and is becoming a major research hotspot, mainly attributed to its powerful algorithms, wide applicability, efficient data processing, accurate feature extraction and low investment costs. However, the application of AI in environmental science and engineering is still in the exploratory stage and its theoretical research and technological innovation are relatively weak. Therefore, IJERPH presents a Special Issue, "Artificial Intelligence in Environmental Science and Engineering", to systematically review the research progress of AI in environmental science and engineering, to collect the latest research results and research hotspots and to explore the main research trends and development prospects. Through the establishment of this academic platform, we will further explore the practical emerging methods and key technologies of AI in the fields of environmental science and engineering, so as to effectively promote the research process of AI in this field and provide a better theoretical reference and technical support for the subsequent research.

The Special Issue welcomes all types of content, including reviews, cutting-edge research and perspectives.

Potential topics include, but are not limited to:

Emerging research directions and methods for artificial intelligence in environmental engineering applications.
Artificial-intelligence-based feature extraction, performance prediction and optimization of chemical functional materials.
Artificial intelligence models for environmental quality monitoring and risk warning.
An artificial-intelligence-based approach to water quality prediction.
Artificial intelligence data-driven models for modelling contaminant transport and removal.
Application of artificial intelligence in the design and optimization of chemically functional materials.
Artificial intelligence in environmental systems modelling.

Dr. Zhenxing Wang
Dr. Kallol Roy
Dr. Cheng Gao
Dr. Lei Huang
Dr. Yunjun Yu
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. International Journal of Environmental Research and Public Health 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 2500 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

artificial intelligence
environmental science and engineering
prediction
optimization
quality monitoring
risk warning
environmental systems modelling

Published Papers (3 papers)

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Editorial

Jump to: Research

4 pages, 276 KiB

Open AccessEditorial

The Application of Machine Learning: Controlling the Preparation of Environmental Materials and Carbon Neutrality

by Zhenxing Wang, Yunjun Yu, Kallol Roy, Cheng Gao and Lei Huang

Int. J. Environ. Res. Public Health 2023, 20(3), 1871; https://doi.org/10.3390/ijerph20031871 - 19 Jan 2023

Cited by 2 | Viewed by 1381

Abstract

The greenhouse effect is a severe global problem [...] Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Environmental Science and Engineering)

Research

Jump to: Editorial

20 pages, 4155 KiB

Open AccessArticle

Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities

by Johanna Karina Solano Meza, David Orjuela Yepes, Javier Rodrigo-Ilarri and María-Elena Rodrigo-Clavero

Int. J. Environ. Res. Public Health 2023, 20(5), 4256; https://doi.org/10.3390/ijerph20054256 - 27 Feb 2023

Cited by 3 | Viewed by 1307

Abstract

The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Environmental Science and Engineering)

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24 pages, 3791 KiB

Open AccessArticle

Fault Diagnosis and Maintenance Countermeasures of Transverse Drainage Pipe in Subway Tunnel Based on Fault Tree Analysis

by Shiyang Liu and Xuefu Zhang

Int. J. Environ. Res. Public Health 2022, 19(23), 15471; https://doi.org/10.3390/ijerph192315471 - 22 Nov 2022

Cited by 4 | Viewed by 1643

Abstract

Transverse drainage pipe, one of the main channels of groundwater behind the lining of subway tunnels, plays an important role in the safety and stability of the tunnel lining structure. For the problem of blocked transverse drainage pipe in a subway tunnel, a fault tree model of blocked transverse drainage pipe in Chongqing subway tunnel was constructed in this paper, the quantitative and qualitative analysis of fault tree was conducted, and countermeasures for maintenance of transverse drainage pipe were proposed. The study finds that, (1) the chemical type of groundwater was mainly CaHCO₃; most of the groundwater is strongly alkaline with pH greater than 8; the groundwater temperature is 20 ± 3 °C; (2) the basic events of blocked transverse drainage pipe have 3 minimum cut sets, and the basic events concrete slurry enters the drainage pipe; groundwater temperature, groundwater pH value, and concentration of anions and cations in groundwater were the main fault factors of blocked transverse drainage pipe; (3) preventive maintenance of transverse drainage pipe during tunnel construction includes construction quality control of drainage pipe and application of anti-crystallized blocking drainage pipe; preventive maintenance of transverse drainage pipe during tunnel operation includes monitoring of groundwater ion concentration, pH, and temperature; and maintenance treatment of transverse drainage pipe during tunnel operation includes physical treatment techniques, such as ultrasonic resonance, and chemical treatment techniques, such as acid-base neutralization reaction. The results of the study have certain guiding significance for the design, construction, and operation of transverse drainage pipe in subway tunnels. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Environmental Science and Engineering)

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