E-ISSN: 2716-5655
CODEN: BDWRAP
Creative Commons Attribution CC BY 4.0

BIG DATA IN WATER RESOURCES ENGINEERING (BDWRE)

This is an open access journal distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

One of the emerging challenges in the 21th century era is collecting and handling ‘Big Data’. The definition of big data changes from one area to other over time. Big data as its name implies is unstructured data that is very big, fast, hard and comes in many forms. Though the applications of big data was confined to information technology before 21st technology, now it is of emerging area in almost all engineering specializations. But for water managers/engineers, big data is showing big promise in many water related applications such as planning optimum water systems, detecting ecosystem changes through big remote sensing and geographical information system, forecasting/predicting/detecting natural and manmade calamities, scheduling irrigations, mitigating environmental pollution, studying climate change impacts etc. This study reviewed the basic information about big data, applications of big data in water resources engineering related studies, advantages and disadvantages of big data. Further, this study presented some of review of literature which has been done on big data applications in water resources engineering.

Frequency: Bi-annual

Journal Flyers

Aims & Scope

At a very basic level, Big Data means we have a lot of data. Water utilities see data from supervisory control and data acquisition (SCADA) systems, including flow statistics, online monitoring, dissolved oxygen (DO) measurements, and air flows, as well as data from laboratory information management systems (LIMS) and computerized maintenance management systems (CMMS), to name several examples. Such data is beneficial, and much of it has been around for years. Unfortunately, the way data is gathered at treatment facilities is often fragmented. There are silos of data in computer systems that don’t always talk to each other. The internet age has ushered in the ability to funnel disparate data into a single, meaningful pool of information that allows water and wastewater treatment plant operators to understand, manage, and use it to optimize plant reliability and performance. Big Data initiatives and new data management tools enable us to turn all that data into understandable, useful information that helps us become more proactive and make better decisions about plant operations.

Topics include but are not limited to;

Smart water management
Big data in water resources engineering
Real-time data management
Water distribution system
Water sustainability
Drought risk management
Irrigation and drainage
Predictive modelling of water resources
Remote sensing and GIS for watershed management
Water pollutants, characteristics and effects
Water supply in specific climates (e.g. arid, semi-arid)
Water security, including water safety plans
Water supply, wastewater and waterborne diseases
Operational intelligence of water supply system
Digitalization of water distribution systems
Artificial intelligence methods in water supply
Climate change impacts on water supply
Integrated management of water resources
Cascading effects between water infrastructure systems
Resource recovery and residuals management
Modelling, systems analysis, machine learning and smart water controls
Water treatment technologies, including reuse and recycling
Storage and distribution of treated non-conventional water resources
Standards for non-conventional water treatment and use
Water utility management including economic and social aspects
The use of domestic and industrial wastewater, or other non-conventional water sources
Water quality and health risk assessment of non-conventional water sources
Planning, design, operation and management of non-conventional water treatment
Regulation and policy for water quality control and water resource management

Peer Review Policy

All peer review is single blind and submission is online via Open Journal Systems (OJS).

Article publishing charge

There is no APC for this journal. All accepted papers shall publish FOC.

Submission charges

There are no submission charges for this journal.
Indexed In Card

Indexed in:

DOAJ SCILET CNKI EBSCO Index Copernicus Wanfang Data

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Usage

  • 6,180K annual downloads/views

Google Citation

  • 0.0 (2025) Impact Factor
  • 295 (2025) Citations
  • 3.6 (2025) CiteScore
  • 0.389 (2025) H Index
  • 0.0 (2025) SNIP
Speed

Speed/Acceptance

  • 90 days avg. from acceptance to online publication

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Creative Commons Attribution
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