York research advances flood risk management with AI

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In a recently published paper, Rahma Khalid, a PhD candidate in the Civil Engineering Department at York University’s Lassonde School of Engineering, and her supervisor, Associate Professor Usman Khan, proposed a promising new model for flood susceptibility mapping (FSM) that incorporates artificial intelligence (AI) machine learning (ML) methods.

Flood susceptibility mapping – the process of identifying potential flood-prone areas based on their physical characteristics – is a valuable technique used to identify areas that are vulnerable to flooding and inform risk mitigation and protection strategies. Unfortunately, conventional FSM methods rely on time-consuming physical and mathematical models that are also limited in their ability to predict flood risk across large regions.

Rahma Khalid
Rahma Khalid

“We have seen that physical and mathematical models can be very inconvenient for flood susceptibility mapping, especially when it comes to analyzing large areas,” says Khalid. “From a research perspective, we know that using machine learning can improve the speed and efficiency of different processes. This is why we proposed a flood susceptibility mapping model that is leveraged by machine learning for more accurate, rapid and reliable results.”

In their paper, titled “Flood susceptibility mapping using ANNs: a case study in model generalization and accuracy from Ontario, Canada,” Khalid and Khan document how they put their idea to the test and utilized an ML model to map out different regions in southern Ontario and determine their flood susceptibility.

Usman Khan
Usman Khan

They did so by using previously gathered data from different regions across southern Ontario, allowing the model to interpret, identify and predict areas that are at risk of flooding.

The model’s performance was also compared against conventional physical and mathematical models, as well as various emerging ML methods.

“When it comes to flood susceptibility mapping in real-world scenarios, machine learning models have not really been used,” says Khalid. “Industry members are also hesitant to apply these models because there is very little information about their accuracy and reliability.”

Khalid and Khan’s proposed model addressed limitations of other FSM models through training and testing that proved it to be a superior method for flood susceptibility mapping, outperforming other models. It even demonstrated novel capabilities that can help advance the future of flood risk management.

“Our model demonstrated a novel ability to accurately predict flood susceptibility, even across areas that we did not provide training data for,” says Khalid. “Knowing this, we can work towards training our model to understand more about different regions and further improve its ability to predict flood susceptibility in larger areas.”

Currently, Khalid and Khan are working on enhancing the performance of their model with a particular focus on improving data resolution, as well exploring the possibility of supplementing their model with additional ML methods.