sustainability-logo

Journal Browser

Journal Browser

Downscaling Sustainable Development Goals (SDGs) for Water Resources Management in Countries, Basins, and Sub-basins

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 1690

Special Issue Editors


E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Illinois, Champaign, IL, USA
Interests: water resources systems analysis; coupled human-hydrology modeling; river basin management; drought management
Special Issues, Collections and Topics in MDPI journals
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: surface water–groundwater–policy multisystem coupled modeling; urban flood simulation; flood early warning and emergency management; machine learning and environmental big data analytics; agent-based model; integrated water resources management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Achieving the United Nations Sustainable Development Goals (SDGs) will require recontextualizing the goals in every country, basin, or sub-basin to cope with the diversity of local conditions and priorities across regions. In particular, several SDGs provide general guidelines for water resource planning and management at the local scale. This Special Issue of Sustainability calls for research and perspective papers that promote research and practice toward downscaling the SDGs for water resource management in countries, basins, and sub-basins, and that facilitate sustainable water resource management everywhere in the world in the light of the SDGs. In general, this Special Issue aims to publish research on the synthesis of country/region experiences, local SDG water indicators, evidence-based decision support tools for local water management practices, and SDG-based guidelines for practices at the basin and sub-basin scales. Specifically, submissions on the following topics in the context of water resource planning and management are encouraged, although submissions are not limited to these topics:

  • Dealing with the diversity of local conditions and complexities across scales;
  • Data collection and analysis for the assessment of the SDGs across scales;
  • State and progress monitoring and assessment towards the SDGs and local sustainable water resource development;
  • Institutional support, especially local involvement and participation of various stakeholder communities;
  • Risks posed by future uncertainties and risk management infrastructural needs in different regions and areas;
  • Interactions between science and practice communities and co-learning between scientists and practitioners;
  • International river basin management via collaboration among riparian

In particular, case studies or pilot studies from a country/region that address any of the topics listed above are expected.

Overall, papers to be published in this Special Issue will contribute to the sustainability literature via systematic methods for assessing spatiotemporal progress towards achieving water-related SDGs, as well as collaborative efforts that enhance co-learning between scientists and stakeholders. Overall, taking water resource planning and management across scales as an example, this Special Issue is expected to provide scientific support for realizing the SDGs.

Prof. Dr. Ximing Cai
Dr. Erhu Du
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. Sustainability 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 2400 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

  • Sustainable Development Goals (SDGs)
  • water resource planning and management
  • river basins
  • downscaling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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

Research

15 pages, 3415 KiB  
Article
Time Series Data Preparation for Failure Prediction in Smart Water Taps (SWT)
by Nsikak Mitchel Offiong, Fayyaz Ali Memon and Yulei Wu
Sustainability 2023, 15(7), 6083; https://doi.org/10.3390/su15076083 - 31 Mar 2023
Cited by 2 | Viewed by 1295
Abstract
Smart water tap (SWT) time series model development for failure prediction requires acquiring data on the variables of interest to researchers, planners, engineers and decision makers. Thus, the data are expected to be ‘noiseless’ (i.e., without discrepancies such as missing data, data redundancy [...] Read more.
Smart water tap (SWT) time series model development for failure prediction requires acquiring data on the variables of interest to researchers, planners, engineers and decision makers. Thus, the data are expected to be ‘noiseless’ (i.e., without discrepancies such as missing data, data redundancy and data duplication) raw inputs for modelling and forecasting tasks. However, historical datasets acquired from the SWTs contain data discrepancies that require preparation before applying the dataset to develop a failure prediction model. This paper presents a combination of the generative adversarial network (GAN) and the bidirectional gated recurrent unit (BiGRU) techniques for missing data imputation. The GAN aids in training the SWT data trend and distribution, enabling the imputed data to be closely similar to the historical dataset. On the other hand, the BiGRU was adopted to save computational time by combining the model’s cell state and hidden state during data imputation. After data imputation there were outliers, and the exponential smoothing method was used to balance the data. The result shows that this method can be applied in time series systems to correct missing values in a dataset, thereby mitigating data noise that can lead to a biased failure prediction model. Furthermore, when evaluated using different sets of historical SWT data, the method proved reliable for missing data imputation and achieved better training time than the traditional data imputation method. Full article
Show Figures

Figure 1

Back to TopTop