Special Issue "Application of Advanced Analytical Techniques to Solve Environmental Problems"

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: 31 March 2020.

Special Issue Editors

Guest Editor
Dr. Ashok Kumar Website E-Mail
Department of Civil & Environmental Engineering, The University of Toledo, USA
Interests: air quality modeling; radon; environmental information technology
Guest Editor
Dr. Akhil Kadiyala Website E-Mail
Department of Civil & Environmental Engineering, The University of Toledo, USA
Interests: environmental modeling and risk assessment; data science; machine learning; artificial intelligence; predictive analytics; cognitive analytics; engineering design; geographic information systems; statistics

Special Issue Information

Dear Colleagues,

The field of applied analytics has grown exponentially during the past decade. This growth is largely driven by the interests of the research and business communities aimed at leveraging considerable improvements in computational resources and analytical techniques in developing scalable solutions for complex real-world problems. This Issue is aimed at providing readers with a comprehensive summary of advanced predictive and cognitive analytical case studies based on the concepts of big data, machine learning, artificial intelligence (AI), geographical information systems, and statistics. This Issue invites the authors to submit papers that exploit the use of advanced analytics in solving environmental related problems. It is strongly recommended that the authors provide a detailed description of the relevant software codes and procedures adopted in their respective studies. The papers may range from database development to the incorporation of AI.

This Special Issue on advanced analytics-based case studies invites you to submit papers across the broader spectrum of environmental science and engineering (e.g., climate change, remote sensing, resource mapping, flood hazard mapping, sustainability, environmental modeling, and online learning). The submission of research work by interdisciplinary teams and multi-country groups is of significant interest.

Dr. Ashok Kumar
Dr. Akhil Kadiyala
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 papers will be 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 semimonthly 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 1800 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

  • Advanced analytics
  • Machine learning
  • Big data
  • Statistics
  • Artificial intelligence
  • Geographic information systems
  • Environmental information technology
  • Environmental management systems
  • Artificial intelligence
  • Air pollution, water pollution, land pollution, indoor air pollution, radon, and monitoring

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Modeling Group Behavior to Study Innovation Diffusion Based on Cognition and Network: An Analysis for Garbage Classification System in Shanghai, China
Int. J. Environ. Res. Public Health 2019, 16(18), 3349; https://doi.org/10.3390/ijerph16183349 - 11 Sep 2019
Abstract
In real life, garbage has caused great pollution to the environment. A garbage classification system is an effective way to manage this issue, and is an innovation in Shanghai, China. Innovation diffusion is the topic of this paper. This study uses a mathematical [...] Read more.
In real life, garbage has caused great pollution to the environment. A garbage classification system is an effective way to manage this issue, and is an innovation in Shanghai, China. Innovation diffusion is the topic of this paper. This study uses a mathematical statistics method to formulate individual bounded rationality, and uses the specific graph structure of a scale-free network to characterize group structure. Then, a model of group behavior is constructed and the simulation experiment is run on the Python platform. The results show that: (1) In the case of general cognitive ability and high value innovation, most individuals in the group will accept the innovation in the process of innovation dissemination in a garbage classification system after several rounds of the game; (2) it is more helpful to improve the cognitive ability of individuals and the true value of innovation for the diffusion of innovation; and (3) the larger a group, the greater the scope of innovation diffusion and the more time is needed. It is helpful to expand the scope and reduce the time of innovation diffusion by increasing connections among individuals. The innovation of this study is the characterization of individual bounded rationality, which has a certain theoretical value. Meanwhile, the research results of this paper have important practical significance for the promotion of garbage classification, which can be used to popularize the concept of garbage classification. Full article
Show Figures

Figure 1

Open AccessArticle
An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms
Int. J. Environ. Res. Public Health 2019, 16(15), 2775; https://doi.org/10.3390/ijerph16152775 - 03 Aug 2019
Abstract
In recent years, with the rapid development of the economy, industrial pollution problems have become more and more serious. This paper constructs an evolutionary game model for industrial pollution between the local governments and enterprises to study the dynamic evolution path of a [...] Read more.
In recent years, with the rapid development of the economy, industrial pollution problems have become more and more serious. This paper constructs an evolutionary game model for industrial pollution between the local governments and enterprises to study the dynamic evolution path of a game system and the evolutionary stable strategy under two punishment mechanisms. The results show that, in a static punishment mechanism (SPM), the strategy between governments and enterprises is uncertain. Moreover, the evolutionary trajectory between governments and enterprises is uncertain. However, under the dynamic punishment mechanism (DPM), the evolution path between governments and enterprises tends to converge to a stable value. Thus, the DPM is more conducive than the SPM for industrial pollution control. Full article
Show Figures

Figure 1

Open AccessArticle
Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions
Int. J. Environ. Res. Public Health 2019, 16(14), 2504; https://doi.org/10.3390/ijerph16142504 - 13 Jul 2019
Cited by 1
Abstract
Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the current situation, a favorable emission reduction policy should be [...] Read more.
Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the current situation, a favorable emission reduction policy should be based on updated information. Various forecasting methods have been developed, but their applications are often limited by insufficient data. Grey system theory is one potential approach for analyzing small data sets. In this study, an improved modeling procedure based on the grey system theory and the mega-trend-diffusion technique is proposed to forecast sulfur dioxide emissions in China. Compared with the results obtained by the support vector regression and the radial basis function network, the experimental results indicate that the proposed procedure can effectively handle forecasting problems involving small data sets. In addition, the forecast predicts a steady decline in China’s sulfur dioxide emissions. These findings can be used by the Chinese government to determine whether its current policy to reduce sulfur dioxide emissions is appropriate. Full article
Show Figures

Figure 1

Back to TopTop