WATERSHED MONITORING EUROPE

WaterShed Monitoring offers a comprehensive range of tailored solutions for organizations that protect, withdraw, distribute, treat and discharge water. Through our scientific expertise and technological tools, we help all links in the water value chain to streamline their processes, to better manage their data and, ultimately, to make better decisions in everyone’s best interest.

Along with consulting, IT integration, and training services, our proprietary software Enki is a multifunctional, cloud-based database designed to store, organize, contextualize, analyze, publish and share all types of water-related data. It taps geomatics, statistics, artificial intelligence and deep learning to aggregate and process information. As a secured collaborative platform, this integrated data management tool allows rapid visualization, exploitation and communication of water quality monitoring results.

Enki supports water managers at three levels: (1) Data – Built-in quality assurance and quality control mechanisms help standardize and optimize data acquisition processes; (2) Information – Gathering, structuring, and validating data and metadata all in one place enable treatment to produce reliable information; and (3) Knowledge – Strong data can be turned into actionable insight through decision-support tools and early warning systems.
Consequently, water managers can harness the full potential of Industry 4.0. thanks to Enki’s smart features for regulatory compliance (risk and vulnerability analysis, water safety management plans, etc.), drinking water distribution or sewage collection network management, and two-way communication functionalities allowing for easy notification to users and client relation management.

Leveraging the ergonomic interface and data aggregation capabilities of Enki, Nerthus is an application capable of forecasting conditions favourable to the proliferation of cyanobacteria. It relies on artificial intelligence to extract information from various terrestrial and spatial data sources, including digitally post-processed multispectral satellite images, and deep learning to provide a first indicator of the possible emergence of cyanobacterial efflorescence in a targeted geographical area.