Quantum mechanics

Incremental decision tree

Statistics

Geotechnical engineering

Support vector machine

Geomorphology

Watershed

Bivariate analysis

Entropy (arrow of time)

Erosion

Data mining

Computer science

Earth science

Geology

Alternating decision tree

Physics

Naive Bayes classifier

Mathematics

Mining engineering

Hydrology (agriculture)

Decision tree learning

Mathematical analysis

Artificial intelligence

Decision tree

Tree (set theory)

Logistic model tree

Gully erosion

Logistic regression

Machine learning

Geoscience Frontiers

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory, especially in the Northern provinces. A number of studies have been recently undertaken to study this process and to predict it over space and ultimately, in a broader national effort, to limit its negative effects on local communities. We focused on the Bastam watershed where 9.3% of its surface is currently affected by gullying. Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability. However, unlike the bivariate statistical models, their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors. To cope with such weakness, we interpret preconditioning causes on the basis of a bivariate approach namely, Index of Entropy. And, we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely, Alternating Decision Tree (ADTree), Naïve-Bayes tree (NBTree), and Logistic Model Tree (LMT). We dichotomized the gully information over space into gully presence/absence conditions, which we further explored in their calibration and validation stages. Being the presence/absence information and associated factors identical, the resulting differences are only due to the algorithmic structures of the three models we chose. Such differences are not significant in terms of performances; in fact, the three models produce outstanding predictive AUC measures (ADTree ​= ​0.922; NBTree ​= ​0.939; LMT ​= ​0.944). However, the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns. This is a strong indication of what model combines best performance and mapping for any natural hazard – oriented application.

Comparison of machine learning models for gully erosion susceptibility mapping