  Treatment of stagnant water bodies that act as a breeding site for malarial
vectors is a fundamental step in most malaria elimination campaigns. However,
identification of such water bodies over large areas is expensive,
labour-intensive and time-consuming and hence, challenging in countries with
limited resources. Practical models that can efficiently locate water bodies
can target the limited resources by greatly reducing the area that needs to be
scanned by the field workers. To this end, we propose a practical topographic
model based on easily available, global, high-resolution DEM data to predict
locations of potential vector-breeding water sites. We surveyed the Obuasi
region of Ghana to assess the impact of various topographic features on
different types of water bodies and uncover the features that significantly
influence the formation of aquatic habitats. We further evaluate the
effectiveness of multiple models. Our best model significantly outperforms
earlier attempts that employ topographic variables for detection of small water
sites, even the ones that utilize additional satellite imagery data and
demonstrates robustness across different settings.
