Extent of detection of hidden relationships among different hydrological variables during floods using data-driven models.

Understanding of flood dynamics forms the basis for the leading water resource management and flood risk mitigation practices. In particular, accurate prediction of river flow during massive flood events and capturing the hysteretic behavior of river stage-discharge are among the key interests in hydrological research. The literature demonstrates that data-driven models are significant in identifying complex and hidden relationships among dependent variables, without considering explicit physical schemes.

Continuous streamflow monitoring in shared watersheds using advanced underwater acoustic tomography system: a case study on Zayanderud River.

Iran has experienced a severe drought during the past two decades. Increasing water demand and decreasing available water resources have led the country to critical conflicts, particularly in shared watersheds. In these basins, the precise measurement of releasing water from upstream to downstream can be a key data for resolving water conflicts.