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Titlebook: Applications of Machine Learning in Hydroclimatology; Roshan Srivastav,Purna C. Nayak Book 2025 The Editor(s) (if applicable) and The Auth

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期刊全称Applications of Machine Learning in Hydroclimatology
影响因子2023Roshan Srivastav,Purna C. Nayak
视频video
发行地址Interdisciplinary approach integrates concepts from hydrology and machine learning, presenting a holistic perspective .Provides hands-on case studies that showcase real-world applications of the lates
学科分类The Springer Series in Applied Machine Learning
图书封面Titlebook: Applications of Machine Learning in Hydroclimatology;  Roshan Srivastav,Purna C. Nayak Book 2025 The Editor(s) (if applicable) and The Auth
影响因子.Applications of Machine Learning in Hydroclimatology. is a comprehensive exploration of the transformative potential of machine learning for addressing critical challenges in water resources management. The book explores how artificial intelligence can unravel the complexities of hydrological systems, providing researchers and practitioners with cutting-edge tools to model, predict, and manage these systems with greater precision and effectiveness. It thoroughly examines the modeling of hydrometeorological extremes, such as floods and droughts, which are becoming increasingly difficult to predict due to climate change. By leveraging AI-driven methods to forecast these extremes, the book offers innovative approaches that enhance predictive accuracy. It emphasizes the importance of analyzing non-stationarity and uncertainty in a rapidly evolving climate landscape, illustrating how statistical and frequency analyses can improve hydrological forecasts. Moreover, the book explores the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management...To provide practical solutions, the b
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Applications of Machine Learning in Hydroclimatology978-3-031-64403-0Series ISSN 2520-1298 Series E-ISSN 2520-1301
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Strafrechtliche Aspekte der Managerhaftungs. Over the years, various modelling frameworks have been developed and tested worldwide. These modelling frameworks, generally, are classified into three types: physics-based or process-based, black-box or data-driven and conceptual models. The physics-based models try to represent the catchment sc
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