0000000000345366

AUTHOR

Fatemeh Rezaie

0000-0003-1771-6753

showing 2 related works from this author

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

2021

Abstract The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on the erosive power of the water flow, it is an important task the extraction of terrain features from DEM to properly research gully erosion. Alongside, topography is highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density and floristic composition, runoff generation, which ultimately inf…

geographyQE1-996.5geography.geographical_feature_category010504 meteorology & atmospheric sciencesAdvanced land observation satellite (ALOS)Water flowLandformCforestGully erosion susceptibility (GES)ElevationElastic netTerrainCubistGeologyVegetation010502 geochemistry & geophysics01 natural sciencesAdvanced Spaceborne Thermal Emission and Reflection RadiometerGeneral Earth and Planetary SciencesSurface runoffDigital elevation modelGeomorphologyDigital elevation model (DEM)Geology0105 earth and related environmental sciencesGeoscience Frontiers
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Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates

2022

Abstract Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This study aimed to provide a new approach with better performance for landslide mapping and adopting readily available variables. In addition, it investigates the capability of a state-of-the-art model developed using the group method of data handling (GMDH) to spatially model LS. Furthermore, hybridized models of GMDH were developed using different metaheuristic algorithms. The study area was the Bonghwa region of South Korea, for which an accurate landslide inventory dataset is available. We considered a total of 13 spatial covariates (altitude, slope, aspect, topographic wetness index, val…

Topographic Wetness IndexVariablesReceiver operating characteristicMean squared errorGroup method of data handlingmedia_common.quotation_subjectLandslideArtificial intelligence Data-scarcity Factor selection GIS Natural disasterscomputer.software_genreRegressionCovariateData miningcomputerEarth-Surface Processesmedia_commonMathematicsCATENA
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