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ARS Home » Southeast Area » New Orleans, Louisiana » Southern Regional Research Center » Commodity Utilization Research » Research » Publications at this Location » Publication #400754

Research Project: Improved Conversion of Sugar Crops into Food, Biofuels, Biochemicals, and Bioproducts

Location: Commodity Utilization Research

Title: Big data-driven water research towards metaverse

item Uchimiya, Sophie

Submitted to: Water Science and Engineering Journal
Publication Type: Review Article
Publication Acceptance Date: 2/5/2024
Publication Date: 2/13/2024
Citation: Uchimiya, M. 2024. Big data-driven water research towards metaverse. Water Science and Engineering Journal. 17(2):101-107.

Interpretive Summary: As soil health, water quality, and geospatial big data became curated and publicly available, there is a new challenge: data utilization. This study shows how field observations can be combined with open data sources to explain complex water environments composing soils, irrigation systems, and runoff. Virtual simulations can complement field observations by testing a large number of environmental scenarios. Cloud data-based data utilization method is presented to make use of a vast amount of data in agriculture and waterways.

Technical Abstract: Big data is already publicly available to advance water research beyond the traditional controlled laboratory experiments. Artificial intelligence could be used to link experimental results with spatiotemporal biogeochemical phenomena in the aquatic environments. Digital twin and other virtual simulations are particularly useful in water research areas where systematic experiment is not realistic (e.g., climate impact and water-related disasters) or difficult to design and interpret (e.g., pollutant and carbon/nutrient cycles in estuary, soils, and sediments). This perspective will compare the current state of data-driven water research in environmental and agricultural fields. Success (in environmental bioinformatics) and limitations in environmental field will be discussed, and potential pathways will be described to drive environmental cheminformatics. Data-driven water research could realize early warning and disaster relief simulations for environmental catastrophe (including drinking water contamination); elucidate master variables controlling the environmental risk of diverse classes of contaminants; and provide a methodology to link food, energy, and water nexus