A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting

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 formalizing optimizing probabilistic implementations, as well equally important challenge identifying most useful ones among these implementations. Nonetheless, practically-oriented reviews focusing on such methods, how can be effectively exploited above-outlined essential endeavor, currently missing from hydrological literature. This absence holds despite pronounced intensification research efforts for benefitting this same It also substantial progress that has recently emerged, especially field post-processing, which traditionally provides hydrologists with Herein, we aim to fill specific gap. In our review, emphasize key ideas information lead effective popularizations, an emphasis support successful future implementations further scientific developments. forward-looking direction, identify open questions propose explored future.