Early Warning Embedded in Intelligent Web-based Workflow for River Monitoring through Earth Observation and AI

The exploitation of rivers and hydropower reservoirs involves daily monitoring of the water resources, the meteorological conditions, the status of the coast, the flood areas, etc. Providing with timely and easy to consume information, analytics and early warnings for current and upcoming statuses or events helps water resources managers and high level officials to adequately observe and plan operations for sustainable development of river areas. We present an intelligent web-based workflow that combines different methods of AI, e.g. linked data, deep learning and resoning, to provide an integrated information system that ensures interoperability between spatial information of GIS systems, remote sensing information, symbolic and numerical data like meteorological data and proprietary measurements and creates an actionable knowledge value chain for the needs of rivers and hydropower reservoirs exploitation with embedded early warning capability. We show how hydrodynamic modelling using Telemac with forecasted water economic data, produced from earth observation and in-situ measurements applied to a series of neural network architectures, derive predictive river models, that are integrated into the work-flow and made available for querying, reviewing, projecting the changes in the navigational conditions of navigable rivers, geo-spatial visualization on GIS. The intelligent work-flow further provides with functional features like forecasts generation for river discharge, turbidity, water level, alerting and querying of a variety of correlations and synchronized visualizations in tables, graphs and GIS maps. It helps improve the
operational efficiency by providing ability to interact with and view all water resources management information at ones, ensures accuracy and decision making ability by correlating historic and forecast data with satellite imagery and data, gives automated forecasting of water economic data using satellite meteorological data, reduce risk through automated alerts. We demonstrate on the example of Danube the advantages of the presented intelligent web-based work-flow for the monitoring of rivers and their environment for sustainable development and planning.

Acknowledgement
This work has been carried out within ESA Contract No 4000133836/21/NL/SC