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May 3
Climate change and population growth are placing increasing burdens on water supplies and infrastructure worldwide. Governments and NGOs possess vast amounts of data on water. Leveraging this data and artificial intelligence (AI) in water management practices can create a better future through sustainable water practices.
Although millions of people are aware of water-related challenges, the water cycle remains a mystery, even to those responsible for understanding how water moves on, above and below the surface. However, advancements in AI can help preserve water supplies, reduce contaminants, ensure more equitable distribution, improve water quality and better protect people from floods and droughts.
AI is already revolutionizing water management by optimizing water utilization and creating a more resilient water infrastructure. Here are eight ways governments and utilities are using AI to shape the future of water management and drive sustainability.
Utilities typically assume that specific components in their water management equipment and infrastructure must be replaced or repaired within set periods. Following this outdated maintenance approach can cause a component to be upgraded or replaced before it is required, or push it past its service life, leading to a catastrophic failure.
Digital twins of water infrastructure integrate AI, the Internet of Things (IoT), edge computing, cloud computing and other technologies, enabling companies to transition from reactive to AI-driven predictive maintenance. These virtual models of actual water systems would allow leaders to compare real-time data with historical data from thousands of similar systems. With this information at hand, utilities can predict when and how specific systems will behave. A large water utility in the United Kingdom is utilizing this technology to decrease the frequency of leak events.
Utilities that use AI predictive maintenance approaches can maximize system uptimes and leverage the entire service life of each component. Applying AI algorithms to data enables them to repair or replace components before they fail, but not so early as to waste time or resources unnecessarily. Leaders can also plan and schedule work for times when demand is low, thereby reducing outages and minimizing the impact on daily users.
Because AI can detect trends hidden within vast amounts of data, it can help utility leaders understand and predict water demand, thereby optimizing water supply throughout the day. Adopting a digital water management strategy enhances distribution networks, enabling utilities to reduce water waste and meet demand more effectively. To maintain a constant and consistent water supply even when repairs are underway, machine learning (ML) algorithms can analyze data in real-time, then adjust water flow and pressure to minimize losses and enhance network performance.
When AI model training considers weather, seasonality and other broad factors, these models can help utility and other government leaders identify and respond to larger, more critical patterns in water use. These patterns then guide their decision-making processes about infrastructure, investments and workforces to support future needs.
AI-enabled platforms and solutions can also help optimize water distribution networks by identifying pressure-related issues, such as leaks, and coordinating a response. When leaks are detected, they can pinpoint the location and dispatch repair crews. Artificial intelligence water monitoring systems can then track progress and advise management about when the work will be complete, allowing them to inform the public.
AI enables water managers to leverage data to monitor water quality, track usage, and identify potential infrastructure issues in water systems. Ideally, managers can leverage the insight they gain to detect and remediate issues before they become crises.
For practical data analysis, utilities commonly utilize IoT and similar technologies to collect the necessary data. This process relies on teams of data scientists and other specialists who are adept at leveraging technology to unearth valuable insights from the collected data. That level of tech talent can be hard to find, so many utilities prefer to upskill existing workers. Depending on the training program, this also equips employees to support initiatives to improve the utility’s capacities in edge computing, cloud computing and other technologies.
Once the tech stack and team are in place, AI-enabled platforms enable utilities to monitor pH levels, turbidity, contaminants, and other quality parameters in real-time. AI utility solutions can also analyze sensor data to detect anomalies, trigger alerts and guide corrective actions to maintain safe and clean water supplies.
Digital twins of water infrastructure combine AI, Internet of Things (IoT), edge computing, cloud computing and other technologies to enable predictive maintenance.
Floods are a regular part of the water cycle. In some places, such as the deltas of the Nile and Mekong rivers, floods occur on a predictable schedule. In less accommodating areas, water managers must juggle data on weather, river levels, climate change and historical flood patterns to predict and mitigate the effects of floods.
For decades, this work was manual. Now, officials can leverage digital twins, IoT, generative AI, and other combinations of AI-enabled water management technologies to predict and mitigate flood risks more accurately. Over the long term, insights from these technologies can guide government leaders as they invest in critical infrastructure to protect their populations from floods, plan for disaster recovery, test preventive measures and better understand the risks associated with floods.
During a flood event, these technologies can give utility leaders critical information and help responders prevent loss of life. After the water recedes, they can also help governments restore water quality and repair damage, thereby improving the quality of life for citizens more quickly.
According to the United States Environmental Protection Agency, water utilities that process drinking water and wastewater are among the largest energy consumers, accounting for 30-40% of total energy usage. The agency encourages the adoption of sustainable and efficient practices, which can make these plants more energy-efficient and can reduce these costs by 15 to 30 percent, with payback periods ranging from a few months to a few years.
Water management technologies, such as AI-driven optimization algorithms, can help leaders optimize treatment processes, adjust aeration rates and minimize energy-intensive operations to reduce energy consumption. One tool for leveraging these algorithms is the digital twin, which enables utility leaders to develop hypotheses about where to save energy and run near-instant tests of their hypotheses using various variables. There are several misconceptions about digital twins, but when coupled with Gen AI, more advanced digital twins can develop and test their theories, then offer suggestions on how managers can optimize the systems they represent.
The actual value of a digital twin lies in how users can access vast amounts of information and insights. In manufacturing lines, treatment plants and other process systems, they merge operational data to accurately depict the system’s current state and operation, allowing detailed analysis and insights into system performance and potential enhancements. A wastewater treatment utility in the United Kingdom adopted this technology, resulting in a 15% reduction in operational expenses.
8 Ways AI in Water Management Creates a Better Future
The world’s growing population increases the need for access to clean water, but the Earth’s changing climate increases the difficulty of providing it. AI-driven water management models, leveraging data gathered through IoT data monetization, can analyze data once considered too disparate to compare, enabling government leaders to assess climate risks more effectively and develop adaptive strategies for managing water supplies.
IoT applications and digital twins combine and analyze data from sensors embedded across the landscape. At the same time, AI helps create virtual replicas that respond to various stimuli in real-time. Users can see how the twin performs normally, then test different scenarios to understand how the system might respond. Digital twins can reveal how systems interoperate, suggest courses of action and offer suggestions about how to improve the original.
These platforms and solutions enable government planners to anticipate and prepare for mitigating the effects of increasingly severe weather events. They can also identify potential vulnerabilities, allowing decision-makers to engage in more proactive planning, preparation and resilience-building measures.
Throughout history, drought has destroyed entire civilizations, forcing humans to abandon cities, engage in wars, and migrate to new lands. It’s an entirely normal part of the water cycle, but it can be devastating to those living in it.
Like many aspects of water management, predicting drought can be challenging, and water managers must juggle vastly disparate forms of data, including weather, water tables, soil moisture, vegetation health, drought patterns, and other historical climate information.
This extensive data analysis is tailor-made for AI in general and Gen AI in particular. Now, governments can leverage digital twins, IoT and other combinations of AI-enabled technologies to predict and mitigate drought risk.
Avoiding droughts altogether may not be possible. Still, AI-enabled platforms can help government leaders adapt to drought in their regions and identify new, more efficient ways of using water, allowing dwindling supplies to serve a greater number of people.
Misunderstanding and misusing water can have profound effects on human life. Fortunately, with the rise of utilities AI solutions for water, we can transform how we preserve and manage our most precious resource.
All crops need water, but not every part of a field requires the same level of watering, timing, or amount of water. For thousands of years, farmers watered their fields intuitively; however, the scale of modern agriculture renders that impossible.
The growing field of AI water management uses IoT irrigation systems to combine information about crop species, weather forecasts, soil moisture, water tables, surrounding vegetation health, sunlight patterns and more to optimize water usage on industrial-scale farms. These systems then deliver the precise amount of water when and where the plants need it, reducing waste and maximizing crop yields.
Achieving this level of precision requires individuals who know how to combine the right technology stack with the right sensors, applications, platforms, and data. That talent can be hard to find, so it makes sense to upskill employees who already understand your business to meet these challenges. They’ll have the chance to assemble the tools while working on projects that add directly to the bottom line.
Misunderstanding and misusing water can have profound effects on human life. Fortunately, with the rise of AI solutions for water utilities, utilities can transform how they preserve and manage our most precious resource.
The key to using these technologies lies not in focusing on any single platform or tool, but in understanding each building block and then choosing the tools that best meet the task at hand.
Sand Technologies’ teams of experts have specialized expertise in the water sector and serve clients worldwide. Contact us to learn how your utility can benefit from an AI-powered water management system.
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