FEATURE Built by AltaML, the agency’s wildfire occurrence prediction system leverages Microsoft Azure Machine Learning to analyse tens of thousands of data points to forecast the likelihood of new fires occurring the next day by region. AltaML is continuously improving the model, which now accurately predicts the likelihood of a new wildfire 80 per cent of the time. “Alberta Wildfire’s new proactive approach enables it to optimise resources, invest more prudently and develop better long-term operational strategies,” says Priest. “Most critically, it maximises the agencies chances of preventing or containing wildfires quickly, thereby minimising the danger to people and property.” Priest adds that data insights can equip city leaders with the knowledge they need to build more resilient infrastructure and better prepare their assets to withstand emergencies too. “If critical infrastructure such as public buildings, roads, bridges and tunnels are poorly maintained, they’re more likely to fail in emergency situations,” he says. “Regularly inspecting these assets is expensive, timeconsuming and resource-intensive, but AI combined with machine vision technology can automate parts of the process and facilitate predictive maintenance. It can also quickly notify the relevant government department about issues such as potholes in roads or cracks in bridges that need urgent attention.” Some cities are encouraging members of the public to play an active role in reporting this type of issue. In Norway, the Stavanger Kommune municipality joined forces with Microsoft partner Bouvet to build a unified data platform and a self-service citizen feedback app using technologies such as Azure OpenAI Service and Power Platform. These tools help its 11,000 employees work more efficiently and make data-driven decisions to enhance everything from road safety to the delivery of multiple services essential to the lives of the municipality’s more than 145,000 inhabitants. Now, the municipality can combine resident feedback with insights from data captured from various sources to prioritise the projects that matter most. For example, it might install traffic calming measures on a road reported as dangerous by residents, or allocate budgets for a new bike lane in a neighbourhood calling for safer cycling. “This optimises how Stavanger Kommune operates, while significantly improving residents’ quality of life,” says Priest. “Importantly, this approach increases residents’ trust in the municipality because they feel heard and can see their opinions matter.” Delivering fast, frictionless and personalised services to the public is a key priority for the City of Burlington in Ontario, Canada, too. Working with Microsoft partner MNP Digital, it used Microsoft Azure OpenAI Service and Power Platform to create the AI-powered MyFiles portal and cut the average time to process approvals for building permits from 15 weeks to between five and seven weeks. “Typically, applying for a permit is a burdensome process – people have to share lots of detailed information, provide specific documents and often visit the city hall,” says Alberta Wildfire uses Microsoft Azure Machine Learning to analyse tens of thousands of data points and predict the likelihood of new fires a day in advance 154 Photo: Alberta Wildfire
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