The pollution of rivers has become one of the major environmental challenges facing cities and regions today. The buildup of waste, the presence of plastics, and the degradation of water quality not only disrupt aquatic ecosystems but can also threaten biodiversity, environmental health, and the ability of governments to manage water resources effectively.
In this context, artificial intelligence and digital twins are opening new possibilities to move from reactive management to a more predictive and efficient model. Arup, a global sustainable development firm, points to the potential of these tools to better understand river behavior, anticipate contamination risks, and prioritize actions where they can have the greatest impact.
Digital twins to anticipate the problem before acting
Traditionally, the management of rivers and aquatic ecosystems has relied on direct observation, on-site measurements, and analysis of historical data. However, the use of real-time data, artificial intelligence, and predictive models is enabling more dynamic approaches and the ability to anticipate problems before they materialize.
Digital twins help shape this new approach. Drawing from information from diverse sources, such as sensors, satellite imagery, weather data, or environmental indicators, these virtual replicas offer a more complete view of a river’s status and the factors that influence its evolution.
In addition to improving the diagnosis of existing problems, digital twins allow the simulation of different scenarios and the evaluation of possible interventions before implementation. In this way, they help anticipate risks, optimize resources, and move toward more efficient and sustainable management of water resources.
AI to clean better and prioritize where to intervene
The usefulness of a digital twin depends on its ability to transform large volumes of information into actionable knowledge for decision-making. In this process, artificial intelligence plays a key role in identifying patterns, detecting anomalies, and generating predictions from data that would otherwise be difficult to analyze.
Applied to water resource management, AI can help identify areas with greater waste accumulation, forecast how certain contaminants will move, or anticipate episodes of higher environmental pressure. This enables faster and more efficient action, prioritizing interventions where they can have the greatest impact.
Beyond optimizing monitoring and maintenance tasks, these tools contribute to better planning of available resources and encourage more sustainable management of river ecosystems. As a result, authorities and operators can adopt preventive strategies, reduce operating costs, and improve the effectiveness of actions aimed at protecting the environment.
The Pasig River: a practical case of smart water management
One example of the potential of these technologies is the project developed for the Pasig River in Manila, one of the world’s most polluted waterways due to plastic waste.
With the goal of improving understanding of the problem and optimizing intervention strategies, a digital twin prototype was developed in collaboration with the Asian Development Bank and RiverRecycle, capable of integrating information from multiple sources, such as geospatial data, weather conditions, environmental variables, and advanced data analysis models.
“Thanks to this tool, it’s possible to visualize how waste behaves along the river, identify accumulation points, and assess the potential impact of various measures before implementing them. This approach helps improve the planning of interventions, optimize the use of available resources, and contribute to more effective management of riverine ecosystems,” said the Water Business Leader for Arup in Spain.
The project demonstrates how the combination of digital twins and artificial intelligence can help policymakers and communities tackle complex environmental challenges through the design of targeted and effective interventions that create new opportunities for the sustainable management of water resources.
