Machine learning (ML) is now widely used across the Earth Sciences and especially its subfield deep learning (DL) has recently enjoyed increased attention in the context of Hydrology. The goal of this session is to highlight the continued integration of ML, and DL in particular, into traditional and emerging Hydrology-related workflows. Abstracts are solicited related to novel theory development, novel methodology, or practical applications of ML and DL in Hydrology. This might include, but is not limited to, the following:

(1) Identifying novel ways for DL in hydrological modelling.
(2) Testing and examining the usability of DL based approaches in hydrology.
(3) Improving understanding of the (internal) states/representations of DL models.
(4) Integrating DL with traditional hydrological models.
(5) Creating an improved understanding of the conditions for which DL provides reliable simulations. Including quantifying uncertainty in DL models.
(6) Clustering and/or classifying hydrologic systems, events and regimes.
(7) Using DL for detecting, quantifying or cope with nonstationarity in hydrological systems and modeling.
(8) Deriving scaling relationships or process-related insights directly from DL.
(8) Using DL to model or anticipate human behavior or human impacts on hydrological systems.
(10) DL based hazard analysis, detection/mitigation, event detection, etc.
(11) Natural Language Processing to analyze, interpret, or condense hydrologically-relevant peer-reviewed literature or social media data or to assess trends within the discipline.