Deep Learning-Based Methods for Groundwater Contamination Identification
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 136
Special Issue Editors
Interests: groundwater simulation; inverse problem; contaminant hydrogeology; intelligent simulation of water environments; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Groundwater is an essential resource for human economic production and livelihood, playing an irreplaceable role in maintaining socio-economic development and ecological balance. However, the increasingly severe problem of groundwater pollution poses a significant threat to the security of groundwater resources. In recent years, with rapid developments in technology, the applications of deep learning in the field of environmental science have been receiving increasing attention. Therefore, how to efficiently identify, predict, and assess groundwater pollution using deep learning methods is currently a hot topic of research. We are delighted to invite you to contribute your innovative findings on "Deep Learning-Based Methods for Groundwater Contamination Identification" to make a contribution to this theme. These papers can include, but are not limited to, the following topics:
(1) Identification of groundwater pollution sources based on deep learning;
(2) Deep learning models for predicting groundwater pollution;
(3) Applications of deep learning methods in the assessment of groundwater quality;
(4) Applications of deep learning methods in the control and remediation of groundwater pollution.
Dr. Simin Jiang
Guest Editor
Dr. Zhenbo Chang
Guest Editor Assistant
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. Water 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 2600 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
- deep learning
- artificial neural network
- groundwater pollution
- contaminant source identification
- groundwater quality assessment
- groundwater pollution control and remediation
