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CLEAN WATER USE AI TO HELP COMMUNITIES FIND THE BEST WELL LOCATION

Predict wells!

Award IBM logo Regional Winner in the IBM Call for Code Global Challenge See more!

We target places with water scarcity (mainly Africa)

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about shape

AI to serve

Our system is able to find the best locations of clean groundwater and evaluate the minerals/chemical composition of that water. Then depending on the GPS location of the device, our AI system will predict the best location

about
about

Hardware to answer

Its mission is to guide the user to the nearest and best well location. It is a 2” device with a microcomputer, and its GPS sensor can detect the user's current location. Because it stores lots of locations precalculated by the AI model, it can then easily guide him or her to the best-nearest location. We built it with lower income communities in mind:

  • - It can be used with no connectivity.
  • - No need for a secure access to power sources (we use ultra low power components, charged for many weeks within one day of sunlight).
  • - Using 4 LEDs as guider arrows towards the right direction.
  • - With the ability to change to the next-best location using a single button.

Satisfying well constraints

Our Machine Learning model is optimized to find the best water location within walking distance. It takes into account many important parameters to drill a successful well for the communities.

Cleanliness

We evaluate the water flow and the land use near each location to make sure that the water produced by the potential well will be clean water, suitable for drinking usage.

Drilling Time

Depending on the soil composition, and the depth needed to access water, drilling time can vary from a few hours to many days or weeks. That's why we only recommend locations where a water well can be drilled in at most a few days

Yield

We evaluate potential upstream sources, proximity to other wells, and consumption in order to ensure a sustainable water production from the water wells locations we recommend

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We are happy
to help

  • Machine learning expertise
  • Robotics specialists
  • Training on IBM Cloud
  • Free for all
  • Open source
  • Easy to contact

120+

Hours of training

25+

Countries covered

3+

team members

1

award winning solution

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about shape

Our
Journey

NOVEMBER 2021

IBM Award

IBM Call for code Regional Winner

AUGUST 2021

Deploying to cloud

Uploading the project to IBM cloud

JUNE 2021

Deploying the model

Building the website and connecting the UI with the trained model

Extracting more features

Extracting more complex features and train the model using those features

Testing the device

Testing the device on predefined locations and adding compass for more reliability

MAY 2021

Building the basic model

Smart building the AI model, detect vegetation area

Preparing the data

Start working on the project, preparing the data (merge maps with focus on the main features)

Making the device

Collecting the electric components and start making the physical device

APRIL 2021

Collecting the data

Collecting the data from different maps, propose different approaches for detection

Making some researches

Discuss with mentors to shape the idea and discover potential problems

Our Expert Team
Members

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Adel SaadEddin

Multiple times World finalist in the famous ICPC coding competition
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Mazen Hassani

Multiple awards winner in Robotics
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Pierre Schwob

Fluent in Chinese and Python. Graduated from the best Engineer school in France

Get in Touch

Contact Information

  • UTCC, 126/1 Vibhavadi Rangsit Rd, Khet Din Daeng, Krung Thep Maha Nakhon 10400

  • +66-979-630-913

  • [email protected]

  • www.wellwellwell.app