Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs

Sediments accumulated in check dams are a valuable measure to estimate soil erosion rates. Here, geographic information systems (GIS) and three machine learning techniques (MARS-multivariate adaptive regression splines, RF-random forest and SVM-support vector machine) were used, for the first time, to predict sediment deposition rate (SR) in check-dams located in six watersheds in SW Spain. There, 160 dry-stone check dams (~ 77.8 check-dams km−2), accumulated sediments during a period that varied from 11 to 23 years. The SR was estimated in former research using a topographical method and a high-resolution Digital Elevation Model (DEM) (average of 0.14 m3 ha−1 year−1). Nine environmental-topographic parameters were calculated and employed as predictors of the SR. The ability of MARS, RF and SVM was evaluated by using a five-fold cross-validation, considering the entire area (ALL), the check dams on the hillslope (HILL) and the valley-bottoms (VALLEY), as well as the three catchments (B, C and D) with the highest number of check dams. The accuracy of the models was assessed by the relative root mean square error (RRMSE) and the mean absolute error (MAE). The results revealed that RF and SVM are able to predict SR with higher and more stable accuracy than MARS. This is evident for the datasets ALL, VALLEY and D, where errors of prediction exhibited by MARS were from 44 to 77% (RRMSE) and from 37 to 62% (MAE) higher than those achieved by RF and SVM, but it also held for the datasets HILL and B where the difference of RRMSE and MAE was 7–10% and 12–17%, respectively.