Modeling and data analysis play a crucial role in understanding the behavior of the water system and in supporting decisions on water management.
It allows to predict the effects of human intervention and climate on the operations and to run scenario analysis. Modeling is vital to create value from data generated by sensor networks.
We develop algorithms for the analysis of data coming from sensors. Machine learning techniques are used to classify data, check data quality and forecast system behavior.
For soil, groundwater and surface water management we apply mechanistic or process-based models. We are investigating coupling of machine learning technology and mechanistic models in hybrid modeling frameworks.