Towards real-time assimilation of crowdsourced observations in hydrological and hydraulic modeling.
Dr. Maurizio Mazzoleni, a visiting researcher from IHE-Delft, The Netherlands, has been carrying on an interesting research topic that makes use of observations of physical variables captured by citizens (through smart phones, tablets, pictures..) to improve understanding and prediction of physical processes. He will give a seminar on the topic, showing an application to assimilation of hydrological variables by citizens to improve flood forecasting.
Time: Tue 2017-09-12 12.00 - 13.00
Location: B21, Brinellvägen 23
Participating: Dr. Maurizio Mazzoleni
In recent years, the continued technological advances have stimulated the spread of low-cost sensors which triggered crowdsourcing as a way to obtain observations of hydrological variables in a more distributed way than the classic static physical sensors. The main advantage of using this type of sensors is that they can be used not only by technicians but also by regular citizens. However, due to their relatively limited reliability and varying accuracy in time and space, crowdsourced observations have not been widely integrated in hydrological and/or hydraulic models for flood forecasting applications. Instead, they have generally been used to validate model results against observations, in post-event analyses.
This seminar will show the benefits of assimilating the crowdsourced observations, coming from a distributed network of heterogeneous physical and social (static and dynamic) sensors, within hydrological and hydraulic models, in order to improve flood forecasting. The results of this study demonstrate that crowdsourced observations can significantly improve flood prediction if properly integrated in hydrological and hydraulic models. This can be a potential application of recent efforts to build citizen observatories of water, in which citizens not only can play an active role in information capturing, evaluation and communication, but also can help improving models and thus increase flood resilience.