范式转换
心态
计算机科学
数据科学
数据管理
数据驱动
人工智能
数据挖掘
哲学
认识论
作者
Guangtao Fu,Dragan Savić,David Butler
出处
期刊:Water Research
[Elsevier]
日期:2024-04-08
卷期号:256: 121585-121585
被引量:5
标识
DOI:10.1016/j.watres.2024.121585
摘要
Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water challenges in a changing world. Building on the historical evolution of water engineering from empirical and theoretical paradigms to the current computational paradigm, we argue that a fourth paradigm, i.e., data-centric water engineering, is emerging driven by recent AI advances. Here we define a new framework for data-centric water engineering in which data are transformed into knowledge and insight through a data pipeline powered by AI technologies. It is proposed that data-centric water engineering embraces three principles – data-first, integration and decision making. We envision that the development of data-centric water engineering needs an interdisciplinary research community, a shift in mindset and culture in the academia and water industry, and an ethical and risk framework to guide the development and application of AI. We hope this paper could inspire research and development that will accelerate the paradigm shift towards data-centric water engineering in the water sector and fundamentally transform the planning and management of water infrastructure.
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