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.

翻译：对作为疟疾病媒育种地的停滞水体的处理是大多数消除疟疾运动的基本步骤。然而,在大面积地区查明这种水体是昂贵、劳动密集型和耗时的,因此在资源有限的国家具有挑战性。能够有效定位水体的实用模型可以大大减少需要实地工作人员扫描的区域,从而以有限的资源为目标。为此目的,我们提议一个实用的地形模型,以容易获得的、全球高分辨率的DEM数据为基础,预测潜在的病媒育种水点的位置。我们调查了加纳的Obuasi地区,以评估各种地形特征对不同类型水体的影响,并发现对水生生境形成有重大影响的特征。我们进一步评估多种模型的有效性。我们的最佳模型大大优于早先利用地形变量探测小水点的尝试,即使是利用更多卫星图像数据并显示不同环境的尝试。