Volume 17 Issue 2
Jun.  2024
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Minori Uchimiya. 2024: Big data-driven water research towards metaverse Minori Uchimiya. Water Science and Engineering, 17(2): 101-107. doi: 10.1016/j.wse.2024.02.001
Citation: Minori Uchimiya. 2024: Big data-driven water research towards metaverse Minori Uchimiya. Water Science and Engineering, 17(2): 101-107. doi: 10.1016/j.wse.2024.02.001

Big data-driven water research towards metaverse Minori Uchimiya

doi: 10.1016/j.wse.2024.02.001
  • Received Date: 2023-04-20
  • Accepted Date: 2024-02-05
  • Available Online: 2024-05-14
  • Although big data is publicly available on water quality parameters, virtual simulation has not yet been adequately adapted in environmental chemistry research. Digital twin is different from conventional geospatial modeling approaches and is particularly useful when systematic laboratory/field experiment is not realistic (e.g., climate impact and water-related environmental catastrophe) or difficult to design and monitor in a real time (e.g., pollutant and nutrient cycles in estuaries, soils, and sediments). Data-driven water research could realize early warning and disaster readiness simulations for diverse environmental scenarios, including drinking water contamination.

     

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