Machine learning and hydrological sciences: A systematic overview of review papers

Water sciences have greatly contributed to the proliferation of machine learning in the twenty-first century, especially through engineering hydrology. This process has consequently necessitated transfer of core theory and knowledge of machine learning to the domain of hydrological sciences. In this regard, it is noteworthy that published academic literature played a substantial role in supporting development and learning of hydrologists. Specifically, research articles (and book sections) that review machine learning concepts and algorithms along with their applications in hydrology bolster progress of science by presenting encapsulated information (e.g, in the form of literature synthesis). Despite the rapid increase in the number and scope of such research articles, a systematic understanding of how this line of research publications has evolved with respect to their scientific context, objectives, and methods is still lacking. Hereby, we present an analysis of review papers in hydrology and machine learning based on a  systematic search strategy. The overview includes analysis of bibliographic information, review types (objective, focus theme, etc.), review methodologies (narrative, systematic, etc.) as well as thematic context (hydrology subjects and machine learning topics). We believe that our analysis can provide important insights into topics and discussions in hydrology and machine learning that need further exploration by hydrologists. Furthermore, the public online library on Zotero (https://www.zotero.org/groups/4828386/machine_learning_hydrology_review_papers/library) might encourage more participation towards sustainable literature search and active reading on this subject at the intersection of two fundamental disciplines, machine learning and hydrology.