showing 4 related works from this author
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.
2020
Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total …
A methodological comparison of head-cut based gully erosion susceptibility models
2020
Abstract A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarjamand watershed in Iran is presented. A database comprised of 15 geo-environmental factors (GEFs) was compiled and used to predict the spatial distribution of 358 gully locations; 70% (251) of which were extracted for training and 30% (107) for validation. A Dempster-Shafer (DS) statistical model was employed to map susceptibility. Next, the results of four kernels (binary logistic, reg logistic, binary logitraw, and reg linear) of a boosted regression tree (BRT) model were combined to increase the efficiency and accuracy of the mapping. Area under receiver operating characteristics (AUROC), tru…
Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques
2019
Abstract This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM) that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS) and the analytic hierarchy process (AHP) multi-criteria decision-making (MCDM) models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete …
Comparison of machine learning models for gully erosion susceptibility mapping
2020
© 2019 China University of Geosciences (Beijing) and Peking University 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 structu…