Knowledge-based validation for hydrological information systems

Abstract

The introduction of systems for automatic data acquisition to monitor and control hydrological basins is a qualitative change in the field of hydrology. The large amount of information available increases the number of processes that can be analyzed with a quantitative approach. In the past, hydrological data validation was done manually by applying the knowledge of experts in the field. This article proposes to solve this problem using AI techniques. As a result, a generic model is defined for the cognitive task of data validation. The model is then applied to a real case.