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iArsenic AI Model for University of Portsmouth Software Engineering Final Year Project

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iArsenic –  Instant Arsenic screening of hand pump tubewells in Bangladesh

Live at https://portsoc.github.io/iArsenic

About

Web-based application to estimate arsenic levels in untested wells in Bangladesh.

Structure:

  1. the main application is a set of static files:
    • the HTML and JS for the page that allows a user estimate arsenic levels
    • JS files with pre-processed data (see below)
    • request log backed by a Google Cloud Function with a database of requests
  2. the estimation is based on input data with measured arsenic levels in wells in Bangladesh; this input data lives in data/
    • this data is processed statistically using one of the available models (see models/)
    • we don't process the data on every user request, but rather we pre-process it so we can satisfy user requests with a simple data lookup
    • a model generates aggregate data files that capture the estimate for every region (i.e. mouza) and every depth stratum and every combination of staining/flooding inputs, these become part of the static files above
  3. every user request is logged in a little Google Cloud database (see server/) for usage analytics and to show impact

Components:

  • data/ — source data

    • it specifies measured arsenic concentrations in tubewells around Bangladesh
  • docs/ — the website hosted in GitHub Pages

  • preprocessing/ — processing scripts

    • these turn data from the source CSV form into something consumed by the website and other tools
    • then there are tools for CLI running of estimates and testing
  • preprocessing/geodata/ – scripts that use Bangladesh geo-boundary data

    • includes experiments with visualization of the various data we have
  • server/ — server for request log database

    • in a Google Cloud Function

Original scripts in R can now be found here.

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iArsenic AI Model for University of Portsmouth Software Engineering Final Year Project

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