標(biāo)題: Titlebook: Understanding Atmospheric Rivers Using Machine Learning; Manish Kumar Goyal,Shivam Singh Book 2024 The Author(s), under exclusive license [打印本頁] 作者: Encomium 時(shí)間: 2025-3-21 17:56
書目名稱Understanding Atmospheric Rivers Using Machine Learning影響因子(影響力)
書目名稱Understanding Atmospheric Rivers Using Machine Learning影響因子(影響力)學(xué)科排名
書目名稱Understanding Atmospheric Rivers Using Machine Learning網(wǎng)絡(luò)公開度
書目名稱Understanding Atmospheric Rivers Using Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Understanding Atmospheric Rivers Using Machine Learning被引頻次
書目名稱Understanding Atmospheric Rivers Using Machine Learning被引頻次學(xué)科排名
書目名稱Understanding Atmospheric Rivers Using Machine Learning年度引用
書目名稱Understanding Atmospheric Rivers Using Machine Learning年度引用學(xué)科排名
書目名稱Understanding Atmospheric Rivers Using Machine Learning讀者反饋
書目名稱Understanding Atmospheric Rivers Using Machine Learning讀者反饋學(xué)科排名
作者: Champion 時(shí)間: 2025-3-21 21:59
Manish Kumar Goyal,Shivam Singhality of life for millions of individuals. While drug discovery of biotherapeutics and biosimilars was originally dominated by small biotechnology companies, today nearly every major pharmaceutical company in the world is engaged in this effort. Biotherapeutic programs present unique challenges to d作者: 可行 時(shí)間: 2025-3-22 03:11
Manish Kumar Goyal,Shivam Singhions and recommendations that pertain to chemical substances. The RCD? is designed to be the first reference book to consult when beginning compliance efforts. Every regulatory or advisory list used in the RCD? is keyed to its source, to help readers who need more detailed information on regulations作者: 迅速飛過 時(shí)間: 2025-3-22 07:27 作者: Gerontology 時(shí)間: 2025-3-22 11:55 作者: 借喻 時(shí)間: 2025-3-22 14:09
Understanding Atmospheric Rivers and Exploring Their Role as Climate Extremes,nt role in climate extremes. The chapter provides a comprehensive overview of ARs, their historical background, formation mechanisms, and characterization in the atmosphere. Tracing the historical evolution of AR research, from foundational studies on “tropospheric rivers” to contemporary satellite-作者: NIL 時(shí)間: 2025-3-22 19:52
Characterization and Impacts of Atmospheric Rivers, resulted in several AR identification techniques across the globe. Observing the impact of ARs and the interest of climate communities across the globe, an international collaborative program Atmospheric River Tracking Method Intercomparison Project (ARTMIP) has been launched to develop a holistic 作者: vertebrate 時(shí)間: 2025-3-22 22:43
Key Characteristics of Atmospheric Rivers and Associated Precipitation,ence regional precipitation patterns. This study explores the spatial and temporal distribution of AR events, their impacts on hydrological cycles, and their association with various atmospheric and oceanic processes. Regions prone to AR influence, like the West Coast of North America and parts of E作者: Desert 時(shí)間: 2025-3-23 04:11
Major Large-Scale Climate Oscillations and Their Interactions with Atmospheric Rivers,rns and regional hydrology. Understanding the intricate interactions between these phenomena is crucial for enhancing climate resilience and managing extreme weather risks. We analyzed the correlations between large-scale climate oscillations (LSCOs) and precipitation extremes potentially influenced作者: GROWL 時(shí)間: 2025-3-23 06:17
Role of Machine Learning in Understanding and Managing Atmospheric Rivers,d time can help in mitigating harmful impacts of these ARs. ARs, characterized by their long and narrow corridors of concentrated moisture transport, present challenges in accurate prediction and understanding due to their intricate spatiotemporal features. Traditional Numerical Weather Prediction (作者: 毗鄰 時(shí)間: 2025-3-23 12:10 作者: 治愈 時(shí)間: 2025-3-23 16:13
2191-530X relevance.This 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 manifestat作者: Interstellar 時(shí)間: 2025-3-23 21:30 作者: 材料等 時(shí)間: 2025-3-24 00:09 作者: mastopexy 時(shí)間: 2025-3-24 05:05
Characterization and Impacts of Atmospheric Rivers,outh America, and Polar Regions. The relationship between ARs and LSCOs (ENSO, MJO, PDO, etc.) can provide valuable insights into the predictability and variability of AR events. The impacts of ARs are multifaceted, encompassing both beneficial and detrimental effects, such as flooding, drought, and作者: Immobilize 時(shí)間: 2025-3-24 10:34 作者: 罐里有戒指 時(shí)間: 2025-3-24 13:44
Major Large-Scale Climate Oscillations and Their Interactions with Atmospheric Rivers, variations capturing these variations more effectively during certain time scales. These findings have important implications for climate forecasting, water resource management, and adaptation strategies. By understanding and leveraging the connections between LSCOs, ARs, and precipitation extremes作者: glisten 時(shí)間: 2025-3-24 16:11
Role of Machine Learning in Understanding and Managing Atmospheric Rivers,olutional architectures, this chapter aims to present AI as a tool to improve the prediction, classification, and tracking of ARs. This paper reviews the potential and challenges associated with AI applications in AR analysis and management, highlighting its pivotal role in enhancing our understandi作者: needle 時(shí)間: 2025-3-24 22:22
Book 2024ntelligence (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 resea作者: 我不明白 時(shí)間: 2025-3-25 00:29
pproximate solutions. Stabilizing properties such as smoothness and shape constraints imposed on the solution are used. On the basis of these investigations, we propose and establish efficient regularization algorithms for stable numerical solution of a wide class of ill-posed problems. In particular, descrip978-90-481-5382-4978-94-015-9482-0作者: 邪惡的你 時(shí)間: 2025-3-25 07:09
Manish Kumar Goyal,Shivam Singhtation. Biotherapeutics are often composed of amino acids whose functionality depends on complex structure. These compounds are measured via protein-protein interactions requiring unique reagents for each drug program. In this chapter, we describe these interactions and focus on the importance of ge作者: BLAND 時(shí)間: 2025-3-25 10:27 作者: 總 時(shí)間: 2025-3-25 12:02
Manish Kumar Goyal,Shivam Singhormation on the electronic versions of the Regulated Chemicals DirectoryTM, contact ChemADVISOR?, Inc. directly (750 William Pitt Way, Pittsburgh, PA 15238, phone 1-800-466-3750). Many companies working on product development need information on what may be regulated in the future. The RCD? provides作者: 使害羞 時(shí)間: 2025-3-25 17:32 作者: 精美食品 時(shí)間: 2025-3-25 22:39
2191-530X g deep learning models exemplifies the practical applications of AI in this domain. The book culminates by underscoring the interdisciplinary nature of AR resea978-3-031-63477-2978-3-031-63478-9Series ISSN 2191-530X Series E-ISSN 2191-5318 作者: 別炫耀 時(shí)間: 2025-3-26 01:23
978-3-031-63477-2The Author(s), under exclusive license to Springer Nature Switzerland AG 2024作者: 話 時(shí)間: 2025-3-26 04:31
Understanding Atmospheric Rivers Using Machine Learning978-3-031-63478-9Series ISSN 2191-530X Series E-ISSN 2191-5318 作者: Favorable 時(shí)間: 2025-3-26 10:35 作者: Infinitesimal 時(shí)間: 2025-3-26 15:54
https://doi.org/10.1007/978-3-031-63478-9Atmospheric River; Climate Extremes; Reanalysis Data; Artificial Intelligence; Deep Learning; Large scale作者: 沙發(fā) 時(shí)間: 2025-3-26 17:24 作者: indicate 時(shí)間: 2025-3-26 22:14
9樓作者: 赤字 時(shí)間: 2025-3-27 04:55
9樓作者: SNEER 時(shí)間: 2025-3-27 06:23
10樓作者: 沉積物 時(shí)間: 2025-3-27 10:02
10樓作者: maverick 時(shí)間: 2025-3-27 17:17
10樓作者: SLING 時(shí)間: 2025-3-27 19:12
10樓
