Building an ML Model to Predict Future Infrastructure Needs of Africa for Policy Makers
  • The Results

Building an ML Model to Predict Future Infrastructure Needs of Africa for Policy Makers

Challenge completed! Results attached!

The African Center for Economic Transformation (ACET) seeks to leverage AI to predict infrastructure needs within Africa to build a better future and create opportunities across countries.

 

The Problem

African governments are using significant portions of public budgets to finance infrastructure, but that infrastructure often responds to past or current needs, not future needs based on expected changes related to climate change, migration, urbanization, etc. Given limited fiscal space, African governments need to use all tools available to ensure the infrastructure being built today best serves the people of Africa for the next 50 to 100 years.

In this Omdena Challenge, a global community collaborated to predict the infrastructure needs of several African countries. We looked into various data sources such as satellite images, socio-economic data, climate, and topological data, population and demographic data, Google Trends, Google business data, social media data (to understand aspirations, needs, and sentiments of people living in the region), and other openly available data. The goal was to model the current situation, past temporal changes in population, infrastructure, etc., then predict future demands of infrastructure.

 

The Project Outcomes

As a team, the aim was to accomplish the following objectives:

  • Building one or multiple models for the future infrastructure needs of Africa (we will limit to selected groups of countries and certain types of infrastructure)
  • Modeling the aspirations of people in the given region of the world
  • Providing recommendations regarding verification approaches and networks to help scale to other countries

 

The Results

The Omdena team looked at the problem from different angles and used all tools to deliver the best solution. Like natural language processing, remote sensing, route planning, data analysis, and machine learning modeling. An interactive dashboard using Streamlit was implemented that makes it easier for the user to go around important and available data and predictions to make better policies and decisions.

A demo of the StreamLit dashboard

The dashboard gives visualizations and predictions to 5 main objectives:

  • Tweet analysis
  • African countries population
  • Electricity access
  • Distance calculations to vital amenities
  • Water stress index

 

To read more about the work done and methodologies used, check the articles attached below.

 

This challenge has been hosted with our friends at

Articles from the project