AI & predictive analytics | Great potential
The increasing sophistication of artificial intelligence is leading to greater opportunity for its application across the hydro industry.
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
Above: Inside view of the Ohsawagawa hydropower station in Japan. Photo by Voith
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 give an overview of the commonly used artificial intelligence models in the field of dams and water engineering: ● Artificial Neural Networks (ANN): ANNs are 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
16 February/March 2026 |
www.waterpowermagazine.com
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 engineers 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.
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