Artificial Intelligence in Disaster Management DOI
Silvio Andrae

Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 73 - 114

Опубликована: Янв. 24, 2025

This chapter examines using artificial intelligence (AI) and deep learning (DL) in disaster management. It describes a paradigm shift towards proactive measures preventing managing natural disasters. Traditional, reactive methods often reach their limits. At the same time, AI-based approaches can improve early warning systems allocate resources more efficiently through analysis of large, heterogeneous data sets ability to recognize complex patterns. The article highlights application DL models, such as Convolutional Neural Networks (CNNs), analyze satellite imagery utility response. Both technical ethical challenges are discussed, particularly protection, bias, transparency models. Finally, framework is presented that provides guidelines for effective responsible use AI management promotes long-term sustainability fairness this area.

Язык: Английский

Rate-induced biosphere collapse in the Daisyworld model DOI Creative Commons
Constantin W. Arnscheidt, Hassan Alkhayuon

Chaos An Interdisciplinary Journal of Nonlinear Science, Год журнала: 2025, Номер 35(2)

Опубликована: Фев. 1, 2025

There is much interest in the phenomenon of rate-induced tipping, where a system changes abruptly when forcings change faster than some critical rate. Here, we demonstrate and analyze tipping classic “Daisyworld” model. The Daisyworld model considers hypothetical planet inhabited only by two species daisies with different reflectivities notable because lead to an emergent “regulation” planet’s temperature. serves as useful thought experiment regarding co-evolution life global environment has been widely used teaching Earth science. We show that sufficiently fast insolation (i.e., incoming sunlight) can cause on go extinct, even if could principle survive at any fixed value among those encountered. Mathematically, this occurs due fact solution forced (nonautonomous) crosses stable manifold saddle point for frozen (autonomous) system. new discovery such classic, simple, well-studied provides further supporting evidence tipping—and indeed, collapse—may be common wide range systems.

Язык: Английский

Процитировано

0
‘Tipping points’ confuse and can distract from urgent climate action DOI
Robert E. Kopp, Elisabeth A. Gilmore, Rachael Shwom

и другие.

Nature Climate Change, Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Язык: Английский

Процитировано

4
Artificial Intelligence in Disaster Management DOI
Silvio Andrae

Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 73 - 114

Опубликована: Янв. 24, 2025

This chapter examines using artificial intelligence (AI) and deep learning (DL) in disaster management. It describes a paradigm shift towards proactive measures preventing managing natural disasters. Traditional, reactive methods often reach their limits. At the same time, AI-based approaches can improve early warning systems allocate resources more efficiently through analysis of large, heterogeneous data sets ability to recognize complex patterns. The article highlights application DL models, such as Convolutional Neural Networks (CNNs), analyze satellite imagery utility response. Both technical ethical challenges are discussed, particularly protection, bias, transparency models. Finally, framework is presented that provides guidelines for effective responsible use AI management promotes long-term sustainability fairness this area.

Язык: Английский

Процитировано

0