IoT and transfer learning based urban river quality prediction

The monitoring of surface water in smart cities can be enhanced with the Internet of Things (IoT) and the use of transfer learning. The former allows the increase in the coverage area and to better exploit this data. The latter has the potential to reduce the need of data collection, which may be costly. In this work, we discuss the potential use of these two domains for the estimation of surface water quality in the Marne River (France). The assessment is made using physico-chemical data from sensors to predict the concentration of fecal indicator bacteria. The results show that the use of transfer learning has the potential to enhance water quality monitoring in smart cities.