RDMAI

Supporting informed action for healthier waterbodies

Our novel use of AI, ML and remote sensing analyzes and connects vast amounts of data to identify patterns of pollution and unlock new insights into the complex factors impacting waterbodies in the UK.

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OVERVIEW

<h4>Developing open-source, scalable, machine learning and remote sensing models to inform effective action to tackle waterbody pollution</h4>

River Deep Mountain AI (RDMAI) is an innovation project funded by the Ofwat Innovation Fund in the UK. The overarching objective of the project is to bring key stakeholders involved in waterbody health together. Collaboratively, we will develop open-source AI/ML and remote sensing models that can inform effective actions to tackle waterbody pollution.

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THE CHALLENGE

The water environment is under huge pressure

Of freshwater species populations has declined in average globally over the last 50 years

It is estimated that 67% avg of natural wetlands have been lost globally during the 20th and 21st century

Of English rivers currently meet the Water Framework Directive´s requirements for good ecological health

It is estimated that as many as three in four rivers could pose a risk to human health in the UK

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THE VISION

<h4>Operationalizing river and coastal health</h4> <p> </p> <ul> <li>Innovating together with the water industry to give our rivers and shores a digital voice.</li> <li>Unlock the value of initiatives across stakeholders by connecting vast amounts of data for informed decision-making.</li> <li>Transition from reporting to prediction, prevention, mitigation and restoration.</li> <li>Discover pollution hotspots and measure impact of interventions.</li> <li>Enable thriving ecosystems and resilient water supplies for future generations.</li> </ul>

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THE APPROACH

<h4>Giving rivers a voice through artificial intelligence, machine learning and remote sensing</h4>

Modeling high-priority pollutants

We apply AI/ML to develop cost-effective and time-efficient ways of modeling high-priority pollution to elevate monitoring practices. By optimizing how we use the data we already have, we improve accuracy and increase understanding of high-priority pollutants, their sources and impact.

Designing an optimized monitoring plan

We use AI/ML models and remote sensing to reduce the financial costs associated with designing and implementing water quality monitoring schemes, informing people on what and where to measure to collect data smarter. We enable stakeholders to understand the most valuable way to monitor target catchments holistically.

Getting more value from continuous monitoring

We use AI/ML models to increase the value and accuracy of the continuous water quality data coming out of section 82 in the Environment Act. We support stakeholders’ ability to sense-check and understand continuous data in a time-efficient and thorough manner.

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OUR AMBITION

<h4><span class="text-white">Preserving one of Earth’s most precious resources</span></h4> <p><span class="text-white">Our holistic and data-driven approach will be key to developing the comprehensive understanding of river health trends and pollution patterns. This understanding is needed to optimize investments and forge targeted partnerships that can tackle river pollution more effectively.</span></p> <p><span class="text-white">Do you want to be part of it?</span></p>

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<h4>About Cognizant Ocean<br> </h4> <p>We take a holistic approach to managing and protecting the ocean’s resources by addressing every aspect of the Blue Economy, from water management, to aquaculture, shipping and energy.<br>  <br> With a united dedication to preserving our rivers and oceans, we strive for a world where aquatic ecosystems thrive, fostering sustainable livelihoods and safeguarding our planet's most precious resources for future generations.</p>

More about Cognizant Ocean

More about Cognizant

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<h3>Blogs</h3>

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<h4>Talk with us about the future of blue life</h4> <p>Each member of our team takes responsibility for creating a culture that enables exceptional experiences.</p>