These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms.
    Author: Panahi M, Gayen A, Pourghasemi HR, Rezaie F, Lee S.
    Journal: Sci Total Environ; 2020 Nov 01; 741():139937. PubMed ID: 32574917.
    Abstract:
    Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy. Based on identified landslide locations, an inventory map was prepared and divided into training and validation data sets (70%/30%). the predicated model outcomes were validated with root mean square error (RMSE), and area under receiver operating characteristic curve (AUC), and pairwise comparison values for the ANFIS, ANFIS-Bee, ANFIS-GWO, SVR, SVR-Bee, and SVR-GWO models were obtained. The area under the curve was obtained with the training and validation data sets. Based on the training data sets, AUC of 80%, 83%, 83%, 69%, 81%, and 80% were obtained for the SVR, SVR-GWO, SVR-Bee, ANFIS, ANFIS-GWO, and ANFIS-Bee models, respectively. For the validation data sets, values of 79%, 82%, 82%, 68%, 79%, and 79%, respectively, were obtained. The SVR-GWO and SVR-Bee models were the most predictive models in terms of constructing the exceptionally focused landslide susceptibility map, with little spatial variation in the highly susceptible classes. Furthermore, the MSE, RMSE, and pairwise comparisons indicated that the SVR-GWO and SVR-Bee models were superior models for this study township. In addition, ANFIS individually was not superior to the ensembles of ANFIS-GWO and ANFIS-Bee for landslide assessment. These landslide susceptibility maps provide a platform for land use planning with an eye toward sustainable development of infrastructure and damage reduction for Icheon Township.
    [Abstract] [Full Text] [Related] [New Search]