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{{See also|Early warning system}}
[[Tsunami]]s can be detected by [[Tsunami warning system]]s. They can make use of AI.<ref>{{cite web|url=https://www.preventionweb.net/go/67242|title=Japanese team develops AI-based system to forecast tsunami and damages|website=www.preventionweb.net}}{{Dead link|date=May 2023 |bot=InternetArchiveBot |fix-attempted=yes }}</ref><ref>{{Cite web|url=https://www.researchgate.net/publication/221057355|title=Artificially Intelligent Tsunami Early Warning System}}</ref> [[Flooding]]s can also be detected using AI systems.<ref>{{cite web|url=https://fortune.com/2019/08/10/artificial-intelligence-wildfires-flooding-rescue/|title=How Artificial Intelligence Could Help Fight Climate Change-Driven Wildfires and Save Lives|website=Fortune}}</ref> [[Wildfire]]s can be predicted using AI systems.<ref>{{cite journal |title=Predictive modeling of wildfires: A new dataset and machine learning approach|first1=Younes Oulad|last1=Sayad|first2=Hajar |last2=Mousannif|first3=Hassan|last3=Al Moatassime|date=March 1, 2019|journal=Fire Safety Journal |volume=104|pages=130–146|doi=10.1016/j.firesaf.2019.01.006|s2cid=116032143|doi-access=free}}</ref><ref>{{Cite web|url=https://www.researchgate.net/publication/261272818|title=(PDF) Artificial intelligence for forest fire prediction}}</ref> [[Wildfire#Detection|Wildfire detection is possible by AI systems]] (i.e. through satellite data, aerial imagery, and personnel position) and can help in the evacuation of people during wildfires.<ref>{{cite journal|title=Using Artificial Intelligence for Safe and Effective Wildfire Evacuations |first1=Xilei|last1=Zhao |first2=Ruggiero|last2=Lovreglio|first3=Erica|last3=Kuligowski|first4=Daniel |last4=Nilsson|date=April 15, 2020|journal=Fire Technology|volume=57|issue=2|pages=483–485 |doi=10.1007/s10694-020-00979-x|s2cid=218801709|doi-access=free}}</ref><ref>{{cite web|url=https://www.cnn.com/2019/12/05/tech/ai-wildfires/index.html |title=How AI is helping spot wildfires faster|author1=Rachel Metz |others=Video by John General |website=CNN|date=5 December 2019 }}</ref><ref>{{cite web |url=https://www.washingtonpost.com/technology/2019/11/06/california-has-million-acres-forest-this-company-is-training-artificial-intelligence-scour-it-all-wildfire/|title=California has 33 million acres of forest. This company is training artificial intelligence to scour it all for wildfire. |first=Peter|last=Holley |work=The Washington Post}}</ref> [[Earthquake warning system|Earthquake detection systems]] are now improving alongside the development of AI technology through measuring seismic data and implementing complex algorithms to improve detection and prediction rates.<ref>{{Cite journal |last1=Mousavi |first1=S. Mostafa |last2=Sheng |first2=Yixiao |last3=Zhu |first3=Weiqiang |last4=Beroza |first4=Gregory C. |date=2019 |title=STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI |journal=IEEE Access |volume=7 |pages=179464–179476 |doi=10.1109/ACCESS.2019.2947848 |s2cid=208111095 |issn=2169-3536|doi-access=free }}</ref><ref>{{Cite journal |last1=Banna |first1=Md. Hasan Al |last2=Taher |first2=Kazi Abu |last3=Kaiser |first3=M. Shamim |last4=Mahmud |first4=Mufti |last5=Rahman |first5=Md. Sazzadur |last6=Hosen |first6=A. S. M. Sanwar |last7=Cho |first7=Gi Hwan |date=2020 |title=Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges |url=https://ieeexplore.ieee.org/document/9218936 |journal=IEEE Access |volume=8 |pages=192880–192923 |doi=10.1109/ACCESS.2020.3029859 |s2cid=226292959 |issn=2169-3536|doi-access=free }}</ref> Earthquake monitoring, phase picking, and seismic signal detection have developed through AI algorithms of [[Deep learning|deep-learning]], analysis, and computational models.<ref>{{Cite journal |last1=Mousavi |first1=S. Mostafa |last2=Ellsworth |first2=William L. |last3=Zhu |first3=Weiqiang |last4=Chuang |first4=Lindsay Y. |last5=Beroza |first5=Gregory C. |date=2020-08-07 |title=Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking |journal=Nature Communications |language=en |volume=11 |issue=1 |pages=3952 |doi=10.1038/s41467-020-17591-w |pmid=32770023 |pmc=7415159 |bibcode=2020NatCo..11.3952M |issn=2041-1723}}</ref> [[Locust]] breeding areas can be approximated using machine learning, which could help to stop locust swarms in an early phase.<ref>{{cite journal |last1=Gómez |first1=Diego |last2=Salvador |first2=Pablo |last3=Sanz |first3=Julia |last4=Casanova |first4=Carlos |last5=Taratiel |first5=Daniel |last6=Casanova |first6=Jose Luis |date=August 15, 2018 |title=Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture |journal=Journal of Applied Remote Sensing |volume=12 |issue=3 |at=036011 |bibcode=2018JARS...12c6011G |doi=10.1117/1.JRS.12.036011 |doi-access=free |s2cid=52230139}}</ref>
==Reception==
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