Mapping Groundwater Potential Through an Ensemble of Big Data Methods.

Area of Science:

• * Hydrogeology and Machine Learning
• * Geospatial Analysis and Environmental Science

Background:

• * Groundwater is essential for safe drinking water in sub-Saharan Africa, especially with climate change impacts.
• * Existing data needs innovative methods for effective groundwater resource assessment.
• * Accurate groundwater potential mapping is critical for water security and planning.

Purpose of the Study:

• * To present a novel methodology for mapping groundwater potential using an ensemble of machine learning classifiers.
• * To develop accurate groundwater potential maps by integrating a large borehole database with geospatial data.
• * To assess the performance of various machine learning algorithms in predicting groundwater presence.

Main Methods:

• * A database of 1848 boreholes was integrated into a Geographic Information Systems (GIS) environment.
• * Twelve machine learning algorithms were trained, validated, and tested using explanatory variables (landforms, soil, geology, etc.).
• * An ensemble approach was employed to combine predictions and incorporate uncertainty measures.

Main Results:

• * Machine learning classifiers achieved high success rates (80-96%) in predicting groundwater potential.
• * Statistical learners, random forests, and support vector machines demonstrated superior performance.
• * The ensemble method enhanced results by providing a measure of uncertainty.

Conclusions:

• * The developed methodology provides an accurate and efficient way to map groundwater potential.
• * This approach is valuable for rural water supply and humanitarian emergencies where field data is limited.
• * Machine learning offers a powerful tool for understanding and managing vital groundwater resources.