Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools
Heerbod Jahanbani, Khandakar Ahmed, Bruce Gu
Abstract
Abstract Data‐driven artificial intelligence (DDAI) prediction has gained much attention, especially in recent years, because of its power and flexibility compared to traditional approaches. In hydrology, streamflow forecasting is one of the areas that took advantage of utilizing DDAI‐based forecasting, given the weakness of the old approaches (e.g., physical‐based approaches). Since many different techniques and tools have been used for streamflow forecasting, there is a new way to explore them. This manuscript reviews the recent (2011–2023) applications of DDAI in streamflow prediction. It provides a background of DDAI‐based techniques, including machine learning algorithms and methods for pre‐processing the data and optimizing or enhancing the machine learning approaches. We also explore the applications of DDAI techniques in streamflow forecasting. Finally, the most common tools for utilizing DDAI techniques in streamflow forecasting are presented.