By Dr Ajith Chandran, Chair, IET Clean Water and Sanitation Working Group
Water is one of the most essential resources on Earth, yet its availability is becoming increasingly threatened. According to the United Nations, over two billion people currently live in countries experiencing high water stress, with global water demand expected to rise by 55% by 2050. Factors such as climate change, population growth, and inefficient water management practices exacerbate the problem. Water scarcity, flooding, and contamination continue to plague communities worldwide. Addressing these issues requires innovative approaches, and artificial intelligence (AI) is emerging as a transformative tool in water management, offering solutions to challenges posed by outdated infrastructure, inefficiencies, and environmental changes.
Global water scarcity: A pressing crisis
Water scarcity is one of the most severe challenges facing humanity. Countries in the Middle East, Africa, and parts of Asia are particularly vulnerable, where prolonged droughts, poor infrastructure, and climate change contribute to decreasing water availability. In urban areas, outdated infrastructure is responsible for significant water loss. For example, in some cities, over 40% of treated water is lost due to leaks in ageing pipelines, leading to inefficiencies and increased stress on the water supply system.
Addressing water loss due to faulty infrastructure requires both extensive investment and cutting-edge technologies. Traditional approaches to identifying leaks and inefficiencies are expensive, time-consuming, and prone to errors. This is where AI can play a pivotal role, helping identify inefficiencies and transform water management.
AI’s role in water management
AI is reshaping the landscape of water management by providing predictive insights, optimising operations, and enabling real-time decision-making. One of AI’s key contributions is its ability to forecast water usage patterns. AI models can accurately predict water demand by analysing historical data and considering variables like weather conditions, population trends, and industrial activities. This helps water utilities allocate resources more effectively, minimising waste while ensuring consistent supply to communities.
Water utilities can also integrate AI systems to monitor and optimise their supply networks. Machine learning algorithms can analyse patterns from vast amounts of data, identifying leaks, inefficiencies, and potential failures in real time. This predictive capability allows utilities to proactively address issues before they result in water losses, thereby reducing operational costs and enhancing water conservation efforts.
Ensuring water quality through real-time monitoring
One of the most critical applications of AI is in water quality monitoring. Traditional methods of detecting water contaminants are labour-intensive and involve periodic testing, which can result in delayed responses to contamination events. AI, on the other hand, can process continuous data streams from IoT-enabled sensors installed in water distribution systems. These sensors monitor variables like pH levels, temperature, and turbidity, detecting changes in water quality in real time. AI algorithms analyse the data, triggering immediate alerts when contaminants or irregularities are detected. This enables rapid intervention, safeguarding public health and preventing widespread contamination.
Such AI-powered systems are also helpful in monitoring natural water bodies like rivers and lakes, which are often at risk from agricultural runoff, industrial waste, and untreated sewage. By predicting contamination risks based on environmental conditions, AI systems help authorities mitigate risks and ensure a cleaner water supply.
AI in flood management and disaster prevention
Apart from addressing water scarcity, AI is proving invaluable in flood management. With climate change increasing the frequency and intensity of floods, accurate forecasting and early warning systems have become critical. AI algorithms can process vast amounts of data from weather forecasts, topographical maps, and historical flood records to predict flood events. Machine learning models can also simulate flood scenarios, helping governments and emergency response teams to better prepare for disasters, allocate resources, and plan evacuations.
Furthermore, AI-driven tools can identify vulnerable areas, model flood risk assessments, and optimise water flow management in dams and reservoirs. These capabilities significantly reduce the economic and humanitarian toll of floods, especially in regions where flood management infrastructure is limited.
Challenges, trends, and future innovations
Despite AI’s potential, several challenges must be addressed to fully realise its benefits in water management. One of the most pressing issues is the need for more access to high-quality data. AI models rely on vast amounts of accurate data to make predictions and recommendations. In many regions, especially in developing countries, data collection infrastructure is inadequate, limiting AI’s effectiveness. Additionally, concerns about the high costs of implementing AI systems in water utilities remain a barrier to widespread adoption.
However, the trends in AI-driven water management are promising. New sensor technology and cloud computing innovations are reducing the costs and complexity of deploying AI systems. As more organisations, governments, and research institutions collaborate on water management initiatives, data sharing and the development of open platforms are expected to accelerate.
The Institute of Engineering and Technology (IET) and its Clean Water and Sanitation Working Group are at the forefront of these efforts. By bringing together experts from various fields, the IET aims to promote the development of innovative technologies and frameworks aimed at achieving Sustainable Development Goal (SDG) 6: Clean Water and Sanitation. The goal is to ensure universal and equitable access to safe and affordable drinking water by 2030.
Collaborations between governments, non-governmental organisations (NGOs), tech companies, and academic institutions are crucial to advancing AI’s role in water management. By leveraging AI and fostering cross-sector collaboration, the world can move closer to meeting water demand while also addressing the effects of climate change, which exacerbates both water scarcity and flooding.
Worldwide experts and various organisations are involved in developing innovative technological solutions for water management. The recently concluded World Water Week Congress in Stockholm in August 2024 presented cutting-edge tools being developed across the world, including such models as digital twins and the use of AI. Indeed, AI has the potential to revolutionise water management, offering solutions to many of the challenges that have plagued the sector for decades. AI is paving the way for more efficient and sustainable use of the world’s most precious resource, from predicting water demand and detecting contaminants in real time to improving flood management systems. However, realising AI’s full potential will require addressing challenges related to data collection, cost, and infrastructure. By fostering collaboration and innovation, organisations like the IET aim to help create a future where technology and sustainable water management practices ensure that safe water is accessible to all.
