Missing values and lack of information in water management datasets: an approach based on Bayesian Networks

Rosa F Ropero, M. Julia Flores, Rafael Rumi

Abstract:

Environmental data often present missing values or lack of information that make modelling tasks
difficult. Under the framework of SAICMA Research Project, a flood risk management system is
modelled for Andalusian Mediterranean catchment using information from the Andalusian
Hydrological System. Hourly data were collected from October 2011 to September 2020, and
present two issues:
In Guadarranque River, for the dam level variable there is no data from May to August 2020,
probably because of sensor damage.
No information about river level is collected in the lower part of Guadiaro River, which make
difficult to estimate flood risk in the coastal area.
In order to avoid removing dam variable from the entire model (or those missing months), or even
reject modelling one river system, this abstract aims to provide modelling solutions based on
Bayesian networks (BNs) that overcome this limitation.