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• W4301400955 abstract "Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably relevant toward addressing the major challenges of formalizing and optimizing probabilistic forecasting implementations, as well as the equally important challenge of identifying the most useful ones among these implementations. Nonetheless, practically-oriented reviews focusing on such concepts and methods, and on how these can be effectively exploited in the above-outlined essential endeavor, are currently missing from the probabilistic hydrological forecasting literature. This absence holds despite the pronounced intensification in the research efforts for benefitting from machine learning in this same literature. It also holds despite the substantial relevant progress that has recently emerged, especially in the field of probabilistic hydrological post-processing, which traditionally provides the hydrologists with probabilistic hydrological forecasting implementations. Herein, we aim to fill this specific gap. In our review, we emphasize key ideas and information that can lead to effective popularizations, as such an emphasis can support successful future implementations and further scientific developments. In the same forward-looking direction, we identify open research questions and propose ideas to be explored in the future." @default.
• W4301400955 created "2022-10-05" @default.
• W4301400955 creator A5003329348 @default.
• W4301400955 creator A5045992049 @default.
• W4301400955 date "2022-10-05" @default.
• W4301400955 modified "2023-10-18" @default.
• W4301400955 title "A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting" @default.
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