Publication Date:
2011-11-27
Description:
ABSTRACT Predicting gully initiation in the catchment scale was done previously by integrating a geographical information system (GIS) with physically-based models, statistical procedures or with knowledge-based expert systems. However, the reliability and validity of applying these procedures are still questionable. In this paper, a data mining (DM) procedure based on decision trees was applied to identify areas of gully initiation risk. Performance was compared with the analytic hierarchy process (AHP) expert system and with the commonly used topographic threshold (TT) technique. A spatial database was used to test the models, composed of a target variable (presence or absence of initial points) and ten independent environmental, climatic and human-induced variables. The following findings emerged: using the same input layers, DM provided better predictive ability of gully initiation points than the application of both AHP and TT. The main difference between the DM and TT was the very high overestimation inherent in TT. In addition, the minimal slope observed for soil detachment was 2°, whereas in other studies it is 3°. This could be explained by soil resistance, which is substantially lower in agricultural fields, while most studies test unploughed soil. Finally, rainfall intensity events of 〉 62.2 mmh -1 (in a period of 30 minutes) were found to have a significant effect on gully initiation. Copyright © 2011 John Wiley & Sons, Ltd.
Print ISSN:
0197-9337
Electronic ISSN:
1096-9837
Topics:
Geography
,
Geosciences
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