| AI & predictive analytics
Challenges and limitations Although AI has ‘great potential’ for managing dams and
water resources, Abulameer et al still identified various challenges and limitations. For example in relation to data quality and availability, uncertainty can cause inconsistencies in AI outcomes and conclusions. Some gauged watersheds need complete water use records, and if unavailable can prevent or hinder machine learning models’ training and subsequent decision-making. The application of AI and the current structures also face technical challenges, such as requiring new and better sensors and data gathering mechanisms, which can be expensive and inaccessible. In addition, depending on historical data to train AI may be an issue. What worked yesterday may be a different topic today, especially as the climate is changing very fast with floods, droughts, storms, hurricanes, and other extreme weather events occurring more frequently than before. Finally, there are also concerns related to data
protection, and the possibility of bias incorporated into algorithms remain issues when introducing AI technologies into water management. Several challenges and limitations also exist in
realtion to technical expertise and capacity. The authors claim that ‘the most acute problem is the shortage of professional employees who would be able to apply and supervise AI solutions in the sphere of water management’. In addition, implementing AI into current water management practices usually entails significant costs associated with improving physical infrastructure and employee training, which is not easily feasible for most water utilities, especially those in the developing world. Ethical and legal considerations also come into play when decisions made with the assistance of artificial intelligence lead to adverse outcomes. Ethical issues involve data ownership, responsibility, and fairness while using AI solutions.
Such issues highlight that more holistic approaches
are needed to gain an understanding of how AI tools and techniques should best be employed now and in the future, Abdulameer et al add. Addressing the most important technical, ethical, and legal questions ‘is crucial for building the public’s trust in technologies that apply AI’, they stress. Nonetheless, they authors conclude that AI in dam and water resource management ‘represents the change needed to address contemporary challenges, including global warming, water scarcity, and structural vulnerability’.
Geological data Writing about AI’s increasing sophistication, in their
research published in the Journal of Infrastructure Intelligence and Resilience, Cao et al explain how AI has injected new vitality into the exploration, intelligence, and digitisation of geological data.
Although obtaining geological information is important
to ensure safe dam construction and operation, traditional geological investigations can be quite extensive. They require numerous professionals to do field trips and are often limited by harsh environments which can make such surveying techniques dangerous and inefficient. In addition, the acquired raw data needs to be processed by experts. AI, the authors explain, can help solve the
aforementioned problems and complete efficient and
accurate geological surveys. Its main application includes surface investigation and internal investigations. As accurate and detailed surveying of the watershed’s
hydrological information is one of the prerequisite tasks in the engineering investigation period, AI can also help with the issue of missing hydrological data, aberrant hydrological data, and complex hydrological information. While the integration of AI into the construction period raises the level of intelligent construction management and ensures the successful completion of construction. As a crucial component of ensuring the long-term safe operation of the project, Cao et al claim AI is more mature and widely studied in operation and maintenance. In particular, the authors say AI can assist people in handling complex and time-consuming tasks that arise during regular workdays.
Digital twin Other research has shown how combining digital twin
technology with deep learning can enhance fault detection, optimise operations, and improve system resilience. A hybrid approach, integrating a digital twin model of a hydropower system with advanced algorithms for real-time monitoring and predictive analysis, has demonstrated remarkable improvements in system performance. As Tan et al explain in their research, such an
approach can be transformative. The digital twin creates a dynamic, real-time digital model of the physical system, enabling simulation and analysis of various operational scenarios. This not only improves predictive accuracy but also allows operators to implement corrective measures before faults materialise. While the deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, can process vast sensor data and identify complex patterns that traditional methods often overlook. The results from this study published in Scientific Reports, show that the proposed method achieved a 12.14% reduction in fault detection time compared to traditional methods. Furthermore, the optimisation of operational parameters led to a 8.97% increase in overall system efficiency and a 5.49% decrease in maintenance costs. In terms of fault detection accuracy, the deep learning-enhanced digital twin system achieved an 72% accuracy rate, significantly higher than the 65% accuracy observed with conventional techniques. The improved model not only enhanced fault detection but also contributed to an 8.03% reduction in energy loss
www.waterpowermagazine.com | February/March 2026| 17
Above: AI is driving global digital transformation
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