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100 1 _aGoyal, Manish Kumar.
_eauthor.
_0(orcid)0000-0001-9777-6128
_1https://orcid.org/0000-0001-9777-6128
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aUnderstanding Atmospheric Rivers Using Machine Learning
_h[electronic resource] /
_cby Manish Kumar Goyal, Shivam Singh.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVIII, 74 p. 30 illus., 29 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
505 0 _aUnderstanding Atmospheric Rivers and Exploring Their Role as Climate Extremes -- Characterization and Impacts of Atmospheric Riversharacterization and Impacts of Atmospheric Rivers -- Key Characteristics of Atmospheric Rivers and Associated Precipitation -- Major Large-Scale Climate Oscillations and their Interactions with Atmospheric Rivers -- Role of Machine Learning in Understanding and Managing Atmospheric Rivers.
520 _aThis book delves into the characterization, impacts, drivers, and predictability of atmospheric rivers (AR). It begins with the historical background and mechanisms governing AR formation, giving insights into the global and regional perspectives of ARs, observing their varying manifestations across different geographical contexts. The book explores the key characteristics of ARs, from their frequency and duration to intensity, unraveling the intricate relationship between atmospheric rivers and precipitation. The book also focus on the intersection of ARs with large-scale climate oscillations, such as El Niño and La Niña events, the North Atlantic Oscillation (NAO), and the Pacific Decadal Oscillation (PDO). The chapters help understand how these climate phenomena influence AR behavior, offering a nuanced perspective on climate modeling and prediction. The book also covers artificial intelligence (AI) applications, from pattern recognition to prediction modeling and early warning systems. A case study on AR prediction using deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR research and the synergy between atmospheric science, climatology, and artificial intelligence.
541 _fUABC ;
_cPerpetuidad
650 0 _aChemical engineering.
650 0 _aEnvironmental engineering.
650 0 _aAtmospheric science.
650 0 _aMachine learning.
650 0 _aClimatology.
650 1 4 _aEnvironmental Process Engineering.
650 2 4 _aAtmospheric Science.
650 2 4 _aMachine Learning.
650 2 4 _aClimate Sciences.
700 1 _aSingh, Shivam.
_eauthor.
_0(orcid)0000-0002-2367-0256
_1https://orcid.org/0000-0002-2367-0256
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031634772
776 0 8 _iPrinted edition:
_z9783031634796
830 0 _aSpringerBriefs in Applied Sciences and Technology,
_x2191-5318
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-63478-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
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