Mapping areas prone to piping using random forest with key explanatory variables

In previous studies, areas prone to soil piping were mapped on the catchment scale by integrating data-mining algorithms and geographical databases. However, this left the question of which input data layers are most influential on soil pipe distribution in semiarid regions, and how they can be exploited for accurate predictions of areas prone to piping. Here, a random forest (RF) procedure was applied to classify areas prone to piping using input data layers selected based on knowledge gained through previous theoretical modeling and empirical measurements. The input data layers were: potential incoming solar radiation; distance from the closest streambank; streambank slope gradient; topographic wetness index; flow accumulation; and vegetation cover as a proxy for the shading effect. An extensive dataset of field measurements (N = 774) was used to train and validate the RF classification procedure. Our results indicated that: (i) RF—based on carefully selected influential input data layers—can be used to map areas prone to soil piping at the catchment scale with high accuracy; (ii) the effect of potential solar radiation and—to a lesser extent—tree shading is noteworthy for soil cracking and subsequent piping development; and (iii) soil pipes were found at short distances (<30 m) from steep bank slopes due to the effect of tension cracks in the streambanks. These observations, over a wide region, further establish the effect of soil cracking, and drying and wetting processes, on soil pipe development in semiarid regions. Future studies applying piping susceptibility mapping at the catchment scale may benefit from using these input layers.