Most of the water on our planet dates back four and a half billion years, meaning it is older than the Sun. It’s even possible that the molecules of the water you drink today once supported the first amoebas, hydrated dinosaurs, or quenched the thirst of Alexander the Great’s armies.
This is because water is a finite resource, existing in a closed cycle on Earth. Like energy, it is neither created nor destroyed—it simply transforms. Today, we face the challenge of managing this cycle sustainably. To aid in this mission, we now turn to technologies like artificial intelligence (AI) to optimize its use and enhance purification processes.
In this article, you will discover how AI helps with:
AI as an ally in the water cycle
Artificial intelligence has become a critical tool for managing the water cycle, optimizing every phase from collection to treatment and distribution. Its ability to analyze vast amounts of data in real-time boosts operational efficiency, minimizes waste, and helps predict issues like leaks or system overloads. Furthermore, AI contributes to more sustainable water use by lowering energy costs and enhancing water security in a world of rising demand and scarcity. Here are some of its most promising applications.
Optimizing treatment in water plants
AI is revolutionizing water treatment by fine-tuning processes like chemical dosing and quality control. Systems automatically adjust parameters based on water conditions, improving operational efficiency. AI can also predict the presence of contaminants, allowing for quicker responses.
Moreover, it learns from historical data, anticipating changes in raw water quality—such as variations in turbidity or contaminant levels—and autonomously adjusts processes. This helps maintain consistent quality standards.
An example: The PRISTINE project, coordinated by ACCIONA, coordinated by ACCIONA, which developed virtual sensors (Soft Sensors) that estimate emerging contaminant concentrations (ECC) in real time. Previously, this was only possible in labs. Installed on an Edge device at the pilot plant, it uses data like flow, pH, and turbidity to automatically adjust treatment processes, keeping pace with growing ECC regulations without lab intervention.
Wastewater management
AI boosts the efficiency of wastewater treatment by automating processes and predicting potential issues. It analyzes both historical and real-time data to optimize energy and chemical use. AI can detect anomalies in sewage networks, preventing blockages and overloads, which reduces environmental impact. This allows treatment plants to recover resources like reusable water and biogas more effectively while cutting costs.
An example: Severn Trent Water in the UK has implemented AI to optimize wastewater flow and prevent overflows. The system forecasts weather conditions, adjusts pumping stations accordingly, and controls water flow to minimize the risk of flooding.
Leak detection and prevention
Water leaks cause significant resource losses. AI systems monitor pressure and flow in real-time, allowing for early leak detection. Predictive algorithms analyze historical data to forecast potential future leaks, enabling companies to take preventive action. This not only prevents water loss but also reduces the cost of unexpected repairs, improving overall network efficiency.
An example: In the US, the Hydro-Logic CivilSense project combines skilled field teams with advanced AI to monitor and detect leaks with 93% accuracy. The goal is to reduce unbilled water losses and operating costs in aging supply systems.
Improving efficiency in water resources management
AI is essential for efficiently managing water resources, predicting demand based on factors like climate, population, and agriculture. Algorithms optimize water distribution to prevent waste and ensure efficient usage. This is particularly crucial in water-scarce areas, where AI helps maximize availability for agriculture, industry, and urban use by better managing reservoirs and irrigation systems.
An example: In California, AI is used to predict water demand in both agricultural and urban areas, adjusting distribution based on climate and population factors. This is especially important in regions prone to drought.
Reduction in energy consumption for water pumping
Water pumping is one of the most energy-intensive tasks in water management. AI can optimize pumping schedules by predicting demand and adjusting operations to off-peak times, reducing energy consumption without compromising water availability. Additionally, smart systems can optimize pumping routes, which not only cuts operational costs but also reduces the carbon footprint of water management.
An example: The DIGIDEL project addresses challenges in desalination and water treatment with advanced AI algorithms. Under the RELEWAT initiative, Reinforcement Learning algorithms are used to optimize energy consumption in pumping wells, allowing them to adjust supply according to fluctuating demand and energy costs.systems, allows wells to adapt supply according to water demand and energy variations.
These applications are just the beginning of how AI will enhance sustainability in the water cycle. If you would like to explore more about the potential of AI and new technologies in biodiversity, check out this article.
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