Geospatial and regression tree analysis to map groundwater depth for manual well drilling suitability in the Zinder region of Niger

SummaryManual and low-tech well drilling techniques have potential to assist in reaching the United Nations’ millennium development goal for water in sub-Saharan Africa. This study used publicly available geospatial data in a regression tree analysis to predict groundwater depth in the Zinder region of Niger to identify suitable areas for manual well drilling. Regression trees were developed and tested on a database for 3681 wells in the Zinder region. A tree with 17 terminal leaves provided a range of ground water depth estimates that were appropriate for manual drilling, though much of the tree’s complexity was associated with depths that were beyond manual methods. A natural log transformation of groundwater depth was tested to see if rescaling dataset variance would result in finer distinctions for regions of shallow groundwater. The RMSE for a log-transformed tree with only 10 terminal leaves was almost half that of the untransformed 17 leaf tree for groundwater depths less than 10 m. This analysis indicated important groundwater relationships for commonly available maps of geology, soils, elevation, and enhanced vegetation index from the MODIS satellite imaging system.

► Suitability maps may help manual well drilling efforts in sub-Saharan Africa.
► Geospatial data were used to predict groundwater depth in an arid region of Niger.
► Natural log transformation compressed variance for greater groundwater depths.
► The transformed regression tree effectively focused on the shallowest depth ranges.
► Regression tree logic was used to create a map of groundwater depth.