Extreme Weather Forecasting and Its Impacts in Togo Using Machine Learning: WeatherAI

This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Lome, Togo Chapter.

The problem

We want to help address damages caused by (extreme) rain and dryness.

Climate change is making the rains and drynesses more extreme in Togo. Many things are easily destroyed/disrupted during the rainy period. Accessing clean water during dryness periods is a challenge in some parts of the country.

Some problems:

• Homes are destroyed
• Life habits are perturbed: being late at rendez-vous/works/school, inability to open stores
• Transit becomes difficult, as it is usual to have water up to the adult waist, making the roads impracticable. 

 

The government and the officials, as well as NGOs, are doing many things to address the situation, but the results we are seeing during extreme weather periods show that citizens are still victims of the situation.

The goals

1. Collect and analyze data

• Collect weather data
• Collect demographic data of the country (we are more interested in the density of each zone)
• Exploratory Data Analysis on the collected data: the target results should be mainly oriented toward bad weather (heavy rains, drought period) and normal weather.
• Explanatory Data Analysis based on the finding of Exploratory Data Analysis

 

2. Predict weather and its impacts

We are interested in models that can make the following predictions: 

• In a given time frame, and for a specific zone, what will be the rainy period and the dryness period? (For the local officials and citizens)
• In a given time frame: Which zones may be subject to heavy rain? Which zones may be subject to high dryness? (For the government and NGOs)
• What may be the duration, violence, and impacts of an identified rain to come? (For the local officials and citizens)
• For an identified dryness period, what may be its duration? Its severity? (For the local officials and citizens)
• Providing weather data on selected locations and selected time frames. (For citizens)
• Other models that Collaborators may deem useful for addressing the situation

We ought to distinguish extreme weather from normal one. The ideal solution should consist of two key deliverables in the case of each model:

• A model ideally deployed on API Gateway, which will respond to HTTP requests (as any API)
• A containerized web application deployed on AWS, to allow end-users to interact with each model’s application. Ideally, we should have a map to depict the model’s predictions. And we can also allow sending alerts (mail, sms) when incoming extreme weather is detected

Time frame: following 07 days, following 30 days, following 12 month

Key zones: district, city, region (there are 05 regions in the country)

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work