Artificial intelligence for climate change adaptation

IF 6.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2022-04-12 DOI:10.1002/widm.1459
S. Cheong, K. Sankaran, Hamsa Bastani
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引用次数: 5

Abstract

Although artificial intelligence (AI; inclusive of machine learning) is gaining traction supporting climate change projections and impacts, limited work has used AI to address climate change adaptation. We identify this gap and highlight the value of AI especially in supporting complex adaptation choices and implementation. We illustrate how AI can effectively leverage precise, real‐time information in data‐scarce settings. We focus on supervised learning, transfer learning, reinforcement learning, and multimodal learning to illustrate how innovative AI methods can enable better‐informed choices, tailor adaptation measures to heterogenous groups and generate effective synergies and trade‐offs.

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适应气候变化的人工智能
虽然人工智能(AI;人工智能(包括机器学习)正在获得支持气候变化预测和影响的牵引力,有限的工作使用人工智能来解决气候变化适应问题。我们发现了这一差距,并强调了人工智能的价值,特别是在支持复杂的适应选择和实施方面。我们说明了人工智能如何在数据稀缺的环境中有效地利用精确、实时的信息。我们专注于监督学习、迁移学习、强化学习和多模式学习,以说明创新的人工智能方法如何能够实现更明智的选择,为异质群体量身定制适应措施,并产生有效的协同效应和权衡。
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来源期刊
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
22.70
自引率
2.60%
发文量
39
审稿时长
>12 weeks
期刊介绍: The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.
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