Journal ArticleDOI
Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion
TLDR
In this article, the performance of seven state-of-the-art machine learning models (SVM with four kernel types, BP-ANN, RF, and BRT) to model the occurrence of gully erosion in the Kashkan-Poldokhtar Watershed, Iran was compared.About:
This article is published in Geomorphology.The article was published on 2017-12-01. It has received 176 citations till now.read more
Citations
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Journal ArticleDOI
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
Jie Dou,Ali P. Yunus,Dieu Tien Bui,Abdelaziz Merghadi,Mehebub Sahana,Zhongfan Zhu,Chi-Wen Chen,Khabat Khosravi,Yang Yang,Binh Thai Pham +9 more
TL;DR: It is suggested that both RF and DT models can be used in other similar non-eruption-related landslide studies in the tephra-deposited rich volcanoes, as they are capable of rapidly generating accurate and stable LSM maps for risk mitigation, management practices, and decision-making.
Journal ArticleDOI
Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
TL;DR: In this article, the importance of effective factors in the occurrence of gully erosion using Boruta algorithm was considered and three factors including land use, distance from river, and clay percent had the most noticeable importance.
Journal ArticleDOI
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models
Ali Azareh,Omid Rahmati,Elham Rafiei-Sardooi,Joel B. Sankey,Saro Lee,Himan Shahabi,Baharin Bin Ahmad +6 more
TL;DR: The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.
Journal ArticleDOI
Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches
Omid Rahmati,Seyed Amir Naghibi,Himan Shahabi,Dieu Tien Bui,Biswajeet Pradhan,Biswajeet Pradhan,Ali Azareh,Elham Rafiei-Sardooi,Aliakbar Nazari Samani,Assefa M. Melesse +9 more
TL;DR: This research investigates the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT), and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs.
Journal ArticleDOI
Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion.
Younes Garosi,Mohsen Sheklabadi,Christian Conoscenti,Hamid Reza Pourghasemi,Kristof Van Oost +4 more
TL;DR: The results showed that the RF model had the highest amount of efficiency, Kappa coefficient, and AUC and the lowest amounts of MAE and RMSE compared with SVM, NB, and GAM when the calibration and validation samples were changed through 10-fold cross-validation technique.
References
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