Author
Listed:
- Eirini Aivazidou
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
- Georgios Banias
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
- Maria Lampridi
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
- Giorgos Vasileiadis
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
- Athanasios Anagnostis
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
- Elpiniki Papageorgiou
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
- Dionysis Bochtis
(Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece)
Abstract
As projections highlight that half of the global population will be living in regions facing severe water scarcity by 2050, sustainable water management policies and practices are more imperative than ever. Following the Sustainable Development Goals for equitable water access and prudent use of natural resources, emerging digital technologies may foster efficient monitoring, control, optimization, and forecasting of freshwater consumption and pollution. Indicatively, the use of sensors, Internet of Things, machine learning, and big data analytics has been catalyzing smart water management. With two-thirds of the global population to be living in urban areas by 2050, this research focuses on the impact of digitization on sustainable urban water management. More specifically, existing scientific literature studies were explored for providing meaningful insights on smart water technologies implemented in urban contexts, emphasizing supply and distribution networks. The review analysis outcomes were classified according to three main pillars identified: (i) level of analysis (i.e., municipal or residential/industrial); (ii) technology used (e.g., sensors, algorithms); and (iii) research scope/focus (e.g., monitoring, optimization), with the use of a systematic approach. Overall, this study is expected to act as a methodological tool and guiding map of the most pertinent state-of-the-art research efforts to integrate digitalization in the field of water stewardship and improve urban sustainability.
Suggested Citation
Eirini Aivazidou & Georgios Banias & Maria Lampridi & Giorgos Vasileiadis & Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2021.
"Smart Technologies for Sustainable Water Management: An Urban Analysis,"
Sustainability, MDPI, vol. 13(24), pages 1-14, December.
Handle:
RePEc:gam:jsusta:v:13:y:2021:i:24:p:13940-:d:704414
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Salem Ahmed Alabdali & Salvatore Flavio Pileggi & Dilek Cetindamar, 2023.
"Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review,"
Sustainability, MDPI, vol. 15(10), pages 1-38, May.
- Evangelos Syrmos & Vasileios Sidiropoulos & Dimitrios Bechtsis & Fotis Stergiopoulos & Eirini Aivazidou & Dimitris Vrakas & Prodromos Vezinias & Ioannis Vlahavas, 2023.
"An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements,"
Sustainability, MDPI, vol. 15(3), pages 1-20, January.
- Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023.
"Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022,"
Resources Policy, Elsevier, vol. 86(PA).
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13940-:d:704414. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.