Real-Time Water Quality Prediction in Belgium

Organisation type

Public administration at regional level

Use case area

Environment

Geographic scope

Belgium

Domain

Environment

Challenge:

The challenge of predicting water quality in real-time stems from the critical need to address environmental sustainability and public health issues in regions like Flanders. Flanders faces unique challenges with water quality due to high population density, intensive agriculture, and industrial activities, which significantly strain water systems. Pollution, nutrient overloading, and changing weather patterns exacerbate these problems, making water quality monitoring a priority for environmental management. However, traditional methods of water quality assessment, often reliant on periodic sampling and laboratory analysis, are slow, resource-intensive, and provide limited temporal coverage. This gap necessitates an innovative, data-driven approach to predict and manage water quality more effectively.

Approach:

To address this challenge, researchers and organisations, such as VITO, VMM, De Watergroup in Flanders have leveraged Linked Data Event Streams (LDES) and live sensor networks to create a real-time water quality prediction framework. The approach integrates advanced sensor technology, incremental machine learning techniques, and linked data principles. Sensors deployed in water bodies collect continuous streams of data on various parameters, such as pH, turbidity, temperature, dissolved oxygen, and nutrient levels. These data streams are then processed using LDES, which standardises and harmonises data from diverse sources, ensuring interoperability and scalability. Incremental machine learning models are employed to analyse this data in near real-time, allowing the system to adapt to new data points and continuously improve prediction accuracy.

Outcomes:

The outcome of this innovative system is a significant improvement in the ability to monitor and predict water quality dynamics in real time. By integrating live sensor data with incremental learning algorithms, decision-makers can receive timely alerts about potential water quality issues, such as pollution spikes or harmful algal blooms. This real-time capability enables proactive interventions to mitigate risks, improving water resource management and safeguarding ecosystems and public health. Additionally, the system's adaptability ensures that it remains robust against evolving environmental conditions and diverse data inputs.

Data sources:

The data sources for this use case include a combination of real-time sensor data, historical water quality datasets, meteorological data, and linked data repositories. Live sensors capture granular and continuous information on water parameters, while historical datasets provide a baseline for training and validating machine learning models. Meteorological data, such as rainfall and temperature patterns, are integrated to understand external influences on water systems. Linked data repositories, facilitated by LDES, ensure that all these diverse datasets are seamlessly interconnected, enabling richer analyses and more accurate predictions.

Additional Information:

Point of Contact:

infovmm [dot] be (info[at]vmm[dot]be)