Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility
Open Access
- 10 June 2020
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 12 (11), 1890
- https://doi.org/10.3390/rs12111890
Abstract
Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors’ effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks’ natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accuracy is GLM, MaxEnt, SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, GLM-ANN-SVM-MaxEnt, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM and MaxEnt-ANN-SVM. The resulting gully erosion susceptibility models (GESMs) are efficient and powerful and could be used to improve soil and water conservation and management.Keywords
This publication has 121 references indexed in Scilit:
- Constructing DEM Based on InSAR and the Relationship between InSAR DEM's Precision and Terrain FactorsEnergy Procedia, 2012
- Height Above the Nearest Drainage – a hydrologically relevant new terrain modelJournal of Hydrology, 2011
- Letter to the Editor: Stability of Random Forest importance measuresBriefings in Bioinformatics, 2010
- Ensemble-based classifiersArtificial Intelligence Review, 2009
- Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, MalaysiaLandslides, 2009
- SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivationNature Genetics, 2008
- Effects of land use and landscape on spatial distribution and morphological features of gullies in an agropastoral area in Sardinia (Italy)CATENA, 2006
- Gully erosion and environmental change: importance and research needsCATENA, 2002
- Receiver Operating Characteristic MethodologyJournal of the American Statistical Association, 2000
- Information Theory and Statistical MechanicsPhysical Review B, 1957