Developing an Automated Water Quality Prediction System using Machine Learning

Local Chapter Kigali, Rwanda Chapter

Coordinated by Rwanda ,

Project background.

Rwanda is a landlocked country located in East Africa, with a population of approximately 13 million people. Despite efforts to improve access to clean water, access remains a critical challenge, particularly in rural areas. According to UNICEF, only 47% of the population has access to basic water services, and only 32% have access to safely managed drinking water services. One of the challenges in ensuring access to clean water is predicting and monitoring water quality. Traditional water quality prediction and monitoring methods are often time-consuming, costly, and may not provide timely and accurate information. This can lead to delays in identifying and addressing water quality issues, putting public health and agricultural productivity at risk.

Machine learning has the potential to revolutionize water quality prediction and monitoring by providing a faster, more accurate, and cost-effective method for predicting water quality. By analyzing large datasets of water quality parameters, machine learning models can identify patterns and relationships between different parameters, enabling accurate predictions of water quality.

Project plan.

  • Week 1

    Research previous work and Data Collection

  • Week 2

    Data Collection

  • Week 3

    Exploratory Data Analysis

  • Week 4

    Preprocessing and feature engineering

  • Week 5

    Model Development

  • Week 6

    Model Training

  • Week 7

    Model Analysis and Interpretation

  • Week 8

    App Development

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