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UC course uses AI models to train next gen flood forecasters

16 July 2025

A new UC course is giving engineering students hands-on AI experience for real-world challenges like flood forecasting, with potential to save lives.

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Photo Caption:UC Dr Alberto Ardid developed the streamflow and flood prediction module of the Applied Modelling and Artificial Intelligence course.

The Applied Modelling and Artificial Intelligence (AI) course teaches Te Whare Wānanga o Waitaha | سԹ (UC) engineering students how to use machine learning to model river flow and predict flooding events, using actual data from the Mohaka River catchment in the Hawke’s Bay.

UC Research Engineer Dr Alberto Ardid, who developed the streamflow and flood prediction module of the course, says it presents a significant shift in engineering education, combining traditional physics-based approaches with modern data-driven methods.

“We use real data to develop a flow forecasting model using a neural network, which is an algorithm you can train to reveal hidden patterns in the data and then use to anticipate the future. This allows students to engage with the challenges of modelling in real, uncertain environments, including issues like missing data, data leakage, and model transferability.”

Dr Ardid says the Mohaka catchment dataset also highlights the social implications of data modelling. The course uses it to simulate scenarios where information is limited — such as in remote or rural regions, where environmental monitoring is scarce.

“When students see their code forecast a rising river in the Mohaka catchment, they realise these tools have real potential to save lives and support communities.

"We developed this course in response to emerging job market demands and the skills future engineers will need. With more frequent and severe weather events, the need for graduates who can apply data and AI to predictive modelling is only growing,” Dr Ardid says.

UC Master of Civil Engineering student Aparna Suseela completed the course last year and says it opened her eyes to the potential applications for AI in civil engineering. “I initially approached the idea of AI integration with some hesitation, particularly around concerns of potential job loss, but this course showed me how AI can actually create new opportunities and make tasks easier and more efficient for engineers.

“It was fascinating to see how basic coding skills combined with engineering knowledge could be used to divert potential disasters. Expanding the knowledge and tuning the model to different locations could be a real game changer in town planning and disaster resilience globally.”

As part of the course, students also complete hands-on projects including wildfire forecasting using meteorological data, clustering geotechnical observations around New سԹbased on soil conditions, and analysing London traffic patterns using real-time data.

The flood-focused module, recently featured in the has been a team effort with and , and inspired a new module for a master's course in machine learning for flood analysis, modelling and management.


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