Artificial Intelligence (AI) has been described as an important driving force bringing about unprecedented changes to dam engineering. With its increasing sophistication opening up fresh opportunities for the digital and intelligent development of dams, the technology is providing new ways to solve issues and improve the ease of engineering exploration.
Keen to educate and direct engineers, politicians, and academics on optimising AI’s influence, in their research published in the Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, Abdulameer et al write that AI has a revolutionary capacity to optimise water governance, and augment predictive and real-time management. It can also guarantee sustainable and resilient practices that can protect water supplies for future generations – especially in the face of challenges such as climate change, growing population rates, and ageing infrastructure.
The authors go on to give an overview of the commonly used artificial intelligence models in the field of water and dam engineering:
- Artificial Neural Networks (ANN): ANNs are frequently employed for risk assessment and predictive modelling. They analyse historical data to predict future dam failures and assess structural health.
- Random Forest Classifiers: This machine learning technique is utilised for environmental impact assessments, helping to evaluate the ecological consequences of dam operations by analysing environmental data such as water quality and biodiversity.
- Support Vector Machines (SVM): SVMs are often used for classification tasks in water resource management, including flood prediction and water quality assessment.
- Decision Trees: These models help in making decisions based on various input parameters, useful in scenarios like water demand forecasting and resource allocation.
- Deep Learning Models: Advanced deep learning techniques are being explored for more complex predictive modelling, including hydrological modelling and real-time anomaly detection in dam safety.
- Reinforcement Learning: This approach is being investigated for optimising reservoir management and operational strategies by learning from the outcomes of past decisions.
- Big Data Analytics: AI models that integrate big data analytics are crucial for processing large datasets from sensors and IoT devices, enhancing the monitoring and management of water resources.
Decision support systems
One of the main developments has been the application of AI in the decision-making process around water resources. AI can strengthen the decision support systems (DSS) and enhance data processing to improve the engagement of the relevant stakeholders. Referring to the computerised systems that aid decision-making, DSS are model-driven, using mathematical models to analyse data to produce results. Both individuals and organisations can use DSS to improve decision quality and efficiency, helping them make informed choices and achieve their strategic goals by providing valuable information and support.
AI has also been used to evaluate the safety risks of dams, with such state-of-the-art approaches improving the capability of dam engineering and water resource managers to forecast, supervise, and manage probable risks more efficiently. Indeed the use of ANN, in a case study for historical dam failure data analysis for future risk assessment of structural failure, highlighted that AI models could: ‘help identify the potential level of dam failure and provide enhanced information to drive effective structural health monitoring and condition assessment practices and operational practices for reducing risks’.
AI is also critical in modelling disaster possibilities about dams and environmental disasters. For example, AI-based simulations have been used to assess the consequences of dam failure on downstream populations and the environment, facilitating evaluation of disaster response measures to address the identified impacts. This predictive approach improves the existing disaster preparedness and response framework, offering useful information for practice.
Challenges and limitations for dam engineering
Although AI has ‘great potential’ for managing dam engineering 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 engineering 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 afore mentioned 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 twins and dam engineering
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 a 8.03% reduction in energy loss and a 14.07% increase in power generation reliability.
According to the authors, future research could further refine these methodologies, explore additional optimisation techniques, and extend the framework’s applicability to other renewable energy systems. Overall, the integration of digital twins and deep learning provides a robust foundation for advancing the operational excellence and sustainability of hydropower plants.
To the rescue
Located in one of the most arid regions of the world, Iran is running out of water. In comparison to a worldwide average of 800mm, its average rainfall is 260mm per year, and with 90% of the country’s water being used for agriculture, effective water management is crucial.
However, according to research by Professor John Abraham from Minnesota’s University of St. Thomas in the US, AI is providing solutions to help tackle this water crisis.
Abraham has been working with Dr. Farzin Salmasi, a water engineering professor at the University of Tabriz in Iran, along with a team of 20 Iranian researchers on the ground. And as their research published in the Iranian Journal of Science And Technology and IWA demonstrates, artificial intelligence can help Iran’s dam engineering sector improve water structure designs and better protect them.
Abraham specialises in the field of fluid mechanics, and his joint research work with Salmasi has looked at optimising the spillway of storage dams. The team has used computer models to train AI to analyse thousands of different designs and determine which ones will help Iranian engineers improve their water structures.
Using AI to make their original ideas better, Salmasi says the joint research work has optimised the stepped spillway of storage dams with the aim of maximising energy dissipation, greatly contributing to the economisation of the designs
With the world “in a race to improve the global water crisis because climate change is adversely affecting the planet’s precipitation patterns”, Abraham says they are hopeful their work will help Iran win its race – improving water management faster than the climate is changing.
“I think we are going to win this race because we have jet power called AI,” he said. “What gives me hope is we are using these new AI techniques to speed up the optimisation process.”
AI, he believes, will give them the momentum they need for hope, progress, and a sustainable tomorrow.
Hydro Pocket
In close partnership with Voith, Ray Sono developed a state-of-the-art IoT solution that enhances the digital capabilities of hydropower plants for greater efficiency and integration with smart grids. Called Hydro Pocket, this system-agnostic platform enables plant operators worldwide to optimise operations, manage data effectively, and reduce maintenance costs while ensuring operational insights are available in real time.
At the beginning of March 2025, Voith successfully commissioned the first Hydro Pocket solution in Japan at the Ohsawagawa hydropower station. Previously, the system could only be monitored remotely via a closed circuit but this new software application enables monitoring from commonly used devices through the Internet, laying the groundwork for further modernisation. The pilot project was implemented in close cooperation with Fuji Electric, which serves as the local customer interface and provided technical support throughout the project.
The implementation of the Hydro Pocket system is reported to have revolutionised operations by providing real-time insights and supporting a more data driven approach. This has significantly improved efficiency and paves the way for future predictive analysis with more data and insights coming from the plant.
The Hydro Pocket dashboard, delivered in Japanese, enables seamless local use and highlights the value of localised, customer-centric digital solutions. Plant owners can monitor, analyse and optimise their hydropower stations through a simple, cloud-based application.
“Key benefits include faster decision-making, increased performance and proactive issue resolution,” explains Dirk Fuchs, Head of HyService Digital & Automation at Voith Hydro.
The real-time access plant data includes energy production, plant operation status and grid connection status. Additionally, it provides historical trend charts, automated standard reports and combined operation and event data, transforming data into actionable insights.
