GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

Abstract

Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

“…Although the literature has mostly reported on the effectiveness of the ensemble techniques, these techniques showed different performances for different problems in different areas. For example, Nhu et al [36] showed that reduced error pruning tree (REPT) performed better in combination with RSS than the bagging and AdaBoost techniques for gully erosion prediction, whereas Pham et al [92] reported that the REPT model performed better with rotation forest and bagging than its combination with the RSS and multiboost for landslide prediction. Different results have also been reported for flood prediction based on the ensemble models [93,94].…”

“…The name bagging is derived from bootstrap aggregating, which is one of the first, most intuitive, and simple ensemble-based algorithms with excellent performance [34][35][36][37]. The diversity of classifiers is received through bootstrap copies of the training dataset.…”

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

“…In general, the regions with higher SPI and STI values have higher potential for groundwater occurrence because they have higher water table [29]. River density presents the drainage capacity which is inverse proportionality of the soil infiltration [10,30,31]. Rainfall is one of the most important factors in groundwater potential model because the more precipitation region are likely to have more groundwater potential [9].…”

“…These results are in line with previous works that demonstrated the advantages of ensemble modeling approaches over single simple modeling. For example, J48 decision tree integrated with Bagging [97] and Naïve Bayes tree integrated with Random Subspace [98] for landslide prediction, RF integrated with different ensemble techniques for gully erosion [31], and alternating decision tree integrated with AdaBoost [29], fisher's linear discriminant function integrated with Bagging [99], RF integrated with Random Subspace [14], and decision stump with different ensemble techniques for groundwater potential mapping [100]. (3), and RMSE (0.504) ( Table 2).…”

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

“…This problem has been addressed through the development and application of machine learning algorithms, which are able to handle large volumes of non-linear and complex data derived from different sources and reported at a variety of scales. These algorithms have been extensively used in natural hazard studies, for example: flooding [23][24][25][26][27][28][29][30][31][32][33], wildfire [34,35], dust storm [36], sinkhole formation [37], drought [38,39], earthquakes [40,41], gully erosion [42][43][44], land/ground subsidence [45,46], groundwater contamination [26,[47][48][49][50][51], and landslides [17,. They can extract informative patterns in historical data to predict future events [79].…”