Sustainable Watershed Management Through IoT-Driven Artificial Intelligence

The contamination of water resources substantially threatens the aquatic environment. Rapid identification of contaminants and recognition of their emission sources in watersheds are crucial for the sustainable management of water resources. Novel IoT technologies coupled with advanced AI strategies may provide efficient tools for real-time detection of contaminants as well as to plan their long-time monitoring. The proposed solution aims to recognize near real-time detection of the pollution incidents, predict the spreading of the pollution more precisely and improve the response for mitigation of the effects in the long run. The primary outcome would be an integrated decision support system utilizing micropollutant measurements along with real-time collected hydrodynamic and meteorological data of a watershed for sustainable water quality management.