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.
