| Title |
AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change |
| ID_Doc |
75774 |
| Authors |
Jain, H; Dhupper, R; Shrivastava, A; Kumar, D; Kumari, M |
| Title |
AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change |
| Year |
2023 |
| Published |
Computational Urban Science, 3, 1 |
| DOI |
10.1007/s43762-023-00100-2 |
| Abstract |
Climate change is one of the most pressing global challenges we face today. The impacts of rising temperatures, sea levels, and extreme weather events are already being felt around the world and are only expected to worsen in the coming years. To mitigate and adapt to these impacts, we need innovative, data-driven solutions. Artificial intelligence (AI) has emerged as a promising tool for climate change adaptation, offering a range of capabilities that can help identify vulnerable areas, simulate future climate scenarios, and assess risks and opportunities for businesses and infrastructure. With the ability to analyze large volumes of data from climate models, satellite imagery, and other sources, AI can provide valuable insights that can inform decision-making and help us prepare for the impacts of climate change. However, the use of AI in climate change adaptation also raises important ethical considerations and potential biases that must be addressed. As we continue to develop and deploy these solutions, it is crucial to ensure that they are transparent, fair, and equitable. In this context, this article explores the latest innovations and future directions in AI-enabled climate change adaptation strategies, highlighting both the potential benefits and the ethical considerations that must be considered. By harnessing the power of AI for climate change adaptation, we can work towards a more resilient, sustainable, and equitable future for all. |
| Author Keywords |
Artificial Intelligence; AI-powered models; Climate change adaptation; Early warning systems; Climate modeling and projections |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001086827400001 |
| WoS Category |
Computer Science, Interdisciplinary Applications; Regional & Urban Planning |
| Research Area |
Computer Science; Public Administration |
| PDF |
https://link.springer.com/content/pdf/10.1007/s43762-023-00100-2.pdf
|