A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models

Abstract In recent years, gully erosion has ceased many development activities and imposed a living threat to local communities residing in southern Iran. Hence, this study sets out to investigate the prediction performance of a machine learning model named the quick, unbiased, efficient statistical tree (QUEST) model for gully susceptibility mapping. Its results were compared to two conventional statistical models: frequency ratio (FR) and evidential belief function (EBF). The area under the receiver operating characteristic (AUROC) and the true skill statistic (TSS) metrics were adopted to assess models' goodness-of-fit and predictive performance in the corresponding training and validation stages. Results revealed that the QUEST model outperforms its counterparts by giving respective AUROC and TSS values of 88.5% and 0.77 in the training stage, followed by EBF (82.3% and 0.65) and FR (80.4% and 0.62). Similarly, the QUEST model showed the highest AUROC and TSS values in the validation stage (83.2% and 0.63, respectively), followed by the EBF (78.6% and 0.63, respectively) and FR (77.1% and 0.58, respectively). Further scrutinization attested that the QUEST model offers a more practical, compendious, and adaptable susceptibility map based on which about 32% of the study area was identified as the high susceptibility zone to gully erosion. Hence, highly gully susceptible areas require pragmatic mitigation plans. In addition, the application of machine learning models for gully erosion merits further studies.