Air Monitoring

  • Using machine learning to improve air quality predictions in Africa

Using machine learning to improve air quality predictions in Africa

Sep 17 2020 Read 355 Times

''Machine learning competitions give you the rush to try out new ideas and things, racing against time and a leader board,” Nikhil Kuma Mishra tells us when we sit with him over a Zoom call to find out about his now 6-year passion for data science. “I am passionate about everything data science,” he quips with a full on smile. His teammate, Darius Moruri, cannot hide the excitement in his voice when talking about Artificial Intelligence (AI). As a self-taught data scientist from Kenya, Darius says he is passionate about competing in data science hackathons: “I enjoy using AI to solve real world problems.”

Nikhil and Darius were brought together by their passion for data science. Not only did they team up - thousands of miles away from each other - in the AirQo Air Quality Forecast Challenge competition, they came 2nd among more than 700 data science competitors across the globe from countries like Uganda, Tanzania, India, Nigeria, Japan, and the UAE.

The competition was organised by the Digital Air Quality, East Africa project, University of Birmingham and the AirQo project from Makerere University, Kampala, in partnership with Zindi – Africa’s largest data science competition platform. It was focused on creating a computer model that would accurately predict air quality in Uganda. This competition was not just about winning the $5,000 prize fund; Nikhil and Darius got the chance to see their solution come to life as they implemented this solution with the AirQo team for one month.

“The most fascinating thing about the AirQo challenge was the ability to implement our winning solution while working with a team of experts at AirQo to predict air pollution, and directly make an impact in people's lives,'' noted Darius, of his once-in-a-lifetime opportunity with Airqo.  “One key achievement that I am most proud of is that now I am more confident in managing a machine learning project from start to end,” he added.

Paul Green, the Project Manager, AirQo noted that Data Science is a growing field and is not widely offered at local universities and many individuals need to teach themselves through such competitions. “This is great but often it is hard to get experience on a real project and to see the model through to completion in the real world. I hope we have been able to help Nikhil and Darius through that process and provided very valuable experience that will help them in their careers not just as competition winners but also with real work experience.”

Until recently there has been a lack of data on air quality across sub-Saharan Africa. The ability to accurately predict air quality over short time periods using AirQo low-cost network of sensors will empower everyone from governments to families to make informed decisions to protect health and guide people’s actions.

“Darius and Nikhil's air quality forecast solution will help AirQo identify serious spikes where pollution levels are higher than expected, investigate the cause and try to minimise it in future,” notes Green. 

Professor Francis Pope, Digital Air Quality, East Africa Project Lead, from the University of Birmingham noted that their aim was to not only increase the number of people who have the skills to look at these challenges and but also to improve our understanding of where air pollution hotspots are. “The competition was a great success, now we have a better prediction of where hotspots of air quality problems are in Kampala, Uganda and we hope we can extend it across Africa in future.”

Celina Lee, CEO of Zindi, noted that there are more data scientists coming up from across the African and other developing markets looking for opportunities to build their skills and showcase what they are capable of. “This was our first competition where we have supported the winners to help implement their solution, and we are so happy with how successful Darius and Nikhil have been in helping AirQo implement their models.''

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