Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence

Abstract

Gully erosion is an environmental problem in arid and semi-arid areas. Gullies threaten the soil and water resources and cause off- and on-site problems. In this research, a new hybrid model combines the index-of-entropy (IoE) model with the weight-of-evidence (WoE) model. Remote sensing and GIS techniques are used to map gully-erosion susceptibility in the watershed of the Bastam district of Semnan Province in northern Iran. The performance of the hybrid model is assessed by comparing the results with from models that use only IoE or WoE. Three hundred and three gullies were mapped in the study area and were randomly classified into two groups for training (70% or 212 gullies) and validation (30% or 91 gullies). Eighteen topographical, hydrological, geological, and environmental conditioning factors were considered in the modeling process. Prediction-rate curves (PRCs) and success-rate curves (SRCs) were used for validation. Results from the IoE model indicate that drainage density, slope, and rainfall factors are the most important factors promoting gullying in the study area. Validation results indicate that the ensemble model performed better than either the IoE or WoE models. The hybrid model predicted that 38.02 percent of the study area has either high or very high susceptible to gullying. Given the high accuracy of the novel hybrid model, this scientific methodology may be very useful for land use management decisions and for land use planning in gully-prone regions. Our research contributes to achieve Land Degradation Neutrality as will help to design remediation programs to control non-sustainable soil erosion rates.

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

“…This is confirmed by the RAF algorithm analysis, which was used to rank the importance of the GECFs for the spatial prediction of gullies in the study area. This result is consistent with [17,109,110]. Roads are impervious surfaces, and they disrupt natural drainage systems due to improper culverts, concentration of surface runoffs, and by altering the hydrological functions of hillslopes, which significantly contribute to overland flow and allow rapid run-off, easily eroding bare soil and causing gullying [111,112].…”

Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

“…The area under receiver operating characteristics (AUROC) curve was used to test the various models. The AUROC curve is a threshold-independent tool for the measurement of predictive performance [101][102][103][104][105][106]. The AUROC indicates the model's predictive accuracy.…”

A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India

“…The landslide susceptibility maps of Darjeeling and Kalimpong districts were prepared by the ensembles of WofE and SVM. These LSMs were then validated using the receiver operating characteristics (ROC) curve, which justifies and evaluates the accuracy of the models [48][49][50][51][52][53][54][55][56]. The ROC curve was prepared along the X and Y-axis.…”