The WPdx Data Standard was created in 2015 by an expert working group and defines a set of basic parameters that should be collected when
This tool harnesses the power of machine learning to make predictions about the status of water points based on the past performance of similar water points in the country.
- Predict which water points are at a higher risk of failure in order to carry out preventative maintenance
- Identify high-risk water points in order to increase monitoring where it is most needed
- Determine which districts have relatively more high-risk water points to more effectively match maintenance budgets with likely need
- Select target country from the drop-down menu
- Select target district(s) from the drop-down menu
- Select whether you want the points on the map colored by the “Last Known Status” (when the last data was collected), or “Today’s Prediction”
- Click Submit
- Access data by clicking “Download Data”
This tool uses available WPDx attributes, such as #water_tech, #water_source, #pay, and others as training data for developing a classification machine learning model. The target variable is #status_id. The models are tuned to optimize the precision (percent of water points that are actually broken) and the recall (percent of all broken water points that are identified as high risk). Predictions are based on adjusting calculating the age of each water point based on #install_year and the current year. A priority for each water point (high/medium/low) is assigned based on the relative number of water points within 1 kilometer and the population within 1 kilometer.
Like all predictions, these predictions are based on probabilities and may not reflect the reality of the status of water points at a given point in time.
We are excited to share two new resources exploring applications of the WPdx Decision Support Tools app: A detailed written tutorial with pictures describing how
Photo caption: WPdx training delivered by MWA to government stakeholder and Sustainable WASH Program implementation partners in March 2023. Photo credit: Selamawit Tiruneh Over the