AI for Good blog

AI-Driven Solutions for Climate Disasters provided by Zindi and ITU at AI for Good

Artificial Intelligence | Environment & Climate change | Health | Innovation & Creativity | Smart Cities

Data scientists are developing prediction models on the Zindi platform to find the best data-driven solutions through data science competitions.

The International Telecommunication Union (ITU) has partnered with Zindi to develop solutions to important global challenges through a series of data science competitions hosted by AI for Good. In these challenges, hundreds of data scientists from around the world are building complex machine learning models to help map potential climate disasters, predict and reduce energy consumption, and address the Sustainable Development Goals (SDGs) in innovative ways.

“Digital inclusion is a shared responsibility, as recently stated by HUAWEI Technologies CEO, David Li. For everyone to be involved and contribute to achieving the sustainable development goals, data science and advanced analytics is a prerequisite,” says Celina Lee, CEO of Zindi.

Here are some of the ways that these projects are making a difference:

Disaster prevention and supporting agriculture around the world

Landslide susceptibility mapping can help local authorities plan and implement sustainable development measures, reduce the risk of landslides, and ensure the safety of communities living in high-risk areas. In this challenge, creating a hazard map with AI allows governments to keep their citizens safe from disasters. See more here.

Accurate and up-to-date crop maps are essential for agriculture as well as other relevant fields, such as natural resources, environment, health, and sustainability. Cropland extent maps are the basic products that allow for practical agricultural applications. Through this competition, Zindi and ITU are enabling a more precise and comprehensive understanding of agricultural landscapes worldwide. To participate click here.

Predicting air pollution for public health

Zindi and ITU have created a data science competition to use machine learning to produce air pollution susceptibility maps in Milan, Italy, which will support local government decision-making to improve the public health and resilience of the city. Zindi believes that through studying our available data and creating predictive models, the world can become a better and safer place for all. To participate in the competition as a data scientist click here.

Making networks more efficient reduces energy consumption

In a world where energy use is responsible for three quarters of fossil fuel consumption, there is a desperate need for ways to reduce this. Using AI, Zindi’s community of data scientists are finding new ways to reduce energy consumption in 5G networks. See more here.

Managing faults in complex telecom networks is an enormous and demanding task. Being able to predict faults in Radio Access Networks (RANs) will lead to reduced costs and improvements in network uptime. ITU and ZINDI believe that a platform like Zindi benefits both the organisations providing the data and the participants of the challenge, who develop real skills by working on real problems. Read more.

Predicting network traffic is another way that AI can improve energy consumption. Zindi is providing the brainpower to find data-driven solutions to tough problems in a timely way. If you are working on network traffic solutions, participate here.

It is estimated that digital technologies directly benefit 70 percent of SDG targets, and this collaboration is showing how advanced technologies like AI can come from anywhere in the world to solve these global challenges. Through this ongoing collaboration with AI for Good, Zindi is providing the tools and platform for everyone who wants to be a part of the solution. To learn more about AI for Good machine learning challenges, visit the website here.

Media Contact: Paul Kennedy Paul[at]
Competition Contact: Tam[at]

ITU AI for Good Communications Officer: theadora.mills[at]
Competition Contact: thomas.basikolo[at]



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