A reliable monitoring approach of floods in small and high slope watersheds

Abstract

An implementation to instantiate a dependable data quality-oriented methodology in the Vinhas Creek monitoring network is presented herein. Redundancy was taken as a core aspect of network reliability. In this instantiation, we implement several machine learning mechanisms to process measurements from the multiple sensors while correlating them according to their geographical position, monitoring timing and the relevant physical processes involved. As an output, we are able to predict the sensor measurements and compare them with the actual sensing value obtained in the monitoring network station. Moreover, in case of any sensor failure, one or more replacement values can be issued. These are important for the correct simulation of the hydrologic and hydraulic processes of the dendritic watershed systems and to predict the inundation characteristics such as levels and flow velocities.