大数据驱动的水研究迈向元宇宙

IF 3.7 Q1 WATER RESOURCES

Water science and engineering Pub Date : 2024-02-13 DOI:10.1016/j.wse.2024.02.001

Minori Uchimiya

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引用次数: 0

摘要

虽然有关水质参数的大数据已经公开，但虚拟仿真尚未充分应用于环境化学研究。数字孪生不同于传统的地理空间建模方法，在系统的实验室/现场实验不现实（如气候影响和与水有关的环境灾难）或难以设计和实时监测（如河口、土壤和沉积物中的污染物和营养物循环）时特别有用。以数据为驱动的水研究可以针对不同的环境情景（包括饮用水污染）实现早期预警和灾难准备模拟。

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Big data-driven water research towards metaverse

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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来源期刊

Water science and engineering WATER RESOURCES-

CiteScore

6.60

自引率

5.00%

发文量

573

审稿时长

50 weeks

期刊介绍： Water Science and Engineering journal is an international, peer-reviewed research publication covering new concepts, theories, methods, and techniques related to water issues. The journal aims to publish research that helps advance the theoretical and practical understanding of water resources, aquatic environment, aquatic ecology, and water engineering, with emphases placed on the innovation and applicability of science and technology in large-scale hydropower project construction, large river and lake regulation, inter-basin water transfer, hydroelectric energy development, ecological restoration, the development of new materials, and sustainable utilization of water resources.