Abstract
A hydrological analysis decision support system is used to support the management of water and to determine the amount of water available for use in a catchment at a given point. The hydrological analysis decision support system rests on applying monthly catchment-related data for calibration, as the ground-based data quality and quantity determine the best outcome of each study. In this paper, we review the hydrological analysis decision support system's solver or algorithm and the traditional monthly calibrated input data method. The introduction of artificial intelligence, cloud computing, satellite data, and cloud mining brings with it a different contribution to data acquisition and how to process the information. The solver, the Out-Of-Kilter algorithm was the preferred option as it can solve the flows in a network problem, and the decision support system needs to be improved to include more than the traditional monthly input data. Furthermore, the use of cloud computing will assist with data acquisition and sharing.