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

Local Chapter Lome, Togo Chapter

Coordinated by Togo ,

Status: Completed

Project Duration: 29 Jul 2023 - 20 Sep 2023

Open Source resources available from this project

Project background.

We are in a rainy period at Lome, and it is usual to have floods in many districts. A recent article from RFI depicts the situation (kindly find the article at https://www.rfi.fr/fr/afrique/20230423-togo-des-quartiers-de-lom%C3%A9-inond%C3%A9s-et-de-nombreuses-familles-sans-logement), where local habitants need to take refuge while forsaking their home as well as their goods and letting them be destroyed by the flood. Having the water come to the adult waist is common in some places. And sometimes, several retention basins didn’t withstand the shock, overflowing and causing flooding in many neighborhoods.

Paradoxically, there are places in the country where it is usual to not have water from the faucet for a long period of time during the dryness period. People need to walk for kilometers to find a well or other water source. Difficult access to clean water sources have a negative impact on people’s health, especially children.

Other than people’s lives, extreme weather is disrupting other sectors, such as the economy. For example, agriculture in Togo depends very much on the period of the year and the weather. So it is easy for farmers to incur loss when the climate becomes extreme and unpredictable.

Project plan.

  • Week 1

    Data collection: Weather data, Weather impacts

  • Week 2

    Data collection: demographic data (we are more interested in density)

  • Week 3

    Data Cleaning

  • Week 4

    Exploratory Data Analysis

  • Week 5

    Exploratory Data Analysis

  • Week 6

    Explanatory Data Analysis | Model Development

  • Week 7

    Model Development

  • Week 8

    Model Deployment

Learning outcomes.

1. Data Collection and Data Wrangling
2. Exploratory Data Analysis, Explanatory Data Analysis, Presentation to an audience
3. Machine Learning and Deep Learning
4. Model Deployment through a web application and an API, on the cloud
5. Containers, Dockers, API Gateways, AWS, CI/CD, MLOps

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