[2] Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets.
[7] These models have found applications in various fields, including economics, customer relations management, financial services, medicine, and the military, among others.
[9] In fact, many data-driven models incorporate machine learning techniques, such as regression, classification, and clustering algorithms, to process and analyse data.
As a result, data-driven models have become an essential topic of discussion and exploration within water resources management and research.
[12] The term "data-driven modelling" (DDM) refers to the overarching paradigm of using historical data in conjunction with advanced computational techniques, including machine learning and artificial intelligence, to create models that can reveal underlying trends, patterns, and, in some cases, make predictions[13] Data-driven models can be built with or without detailed knowledge of the underlying processes governing the system behavior, which makes them particularly useful when such knowledge is missing or fragmented.