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

TL;DR

This paper reviews machine learning concepts and methods for improving probabilistic hydrological forecasting, highlighting recent progress, challenges, and future research directions to enhance implementation and effectiveness.

Contribution

It provides a comprehensive review of machine learning techniques relevant to probabilistic hydrological post-processing, addressing a gap in current literature and guiding future research.

Findings

Identifies key machine learning methods for hydrological probabilistic forecasting

Highlights recent progress and challenges in the field

Proposes future research directions and open questions

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 towards 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 endeavour, 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.