In the coming years, water management will face critical challenges due to the concomitant impact of global warming, population growth, and pollution. There is therefore an urgent need to identify new strategies for river monitoring to support water budget and quality assessment. The main goal of the present research is the development of a new generation of monitoring systems exploiting Earth Observations (EO) and Artificial Intelligence (AI) to provide a qualitative and quantitative characterization of space-time dynamics of river systems. In fact, EO offers increasing potentials in terms space-time resolution and number of sensors, which provide an extraordinary amount of EO-based information. The aim of the present research is to combine available observations in order to improve our ability to describe river systems taking into account the overall dynamics occurring at the river basins scale such as soil moisture state, land use changes, and vegetation state. These information combined river observations and machine learning algorithms may help interpreting the river basin response in along the river leading to a new smart monitoring approach.

Remote sensing, hydrology, Machine Learning

Activities will be carried out at the Department of Civil, Building and Environmental Engineering (DICEA) of the University of Naples Federico II which is a leading institute in hydraulic and hydrological studies. In addition, there will be a close collaboration with the Department of Electrical Engineering and Information Technologies, which is leading the development of new technologies and remote sensing. The environment is a stimulating and challenging one with a strong and significant international dimension.