0000000000359518

AUTHOR

Narges Javidan

showing 3 related works from this author

Data Mining Technique (Maximum Entropy Model) for Mapping Gully Erosion Susceptibility in the Gorganrood Watershed, Iran

2019

Soil erosion is a serious problem affecting most of the countries. This study was carried out in Gorganrood Watershed (Iran), which extends for 10,197 km2 and is severely affected by gully erosion. A gully headcut inven- tory map consisting of 307 gully headcut points was provided by Google Earth images, field surveys, and national reports. Gully conditioning factors including sig- nificant geo-environmental and morphometric variables were selected as predictors. Maximum entropy (ME) model was exploited to model gully susceptibility, whereas the area under the ROC curve (AUC) and draw- ing receiver operating characteristic (ROC) curves were employed to evaluate the performance of the model.…

HydrologyGully erosion Susceptibility Geographic information systems (GIS) Maximum entropy (ME) model Area under the ROC curve (AUC)WatershedReceiver operating characteristicPrinciple of maximum entropySettore GEO/04 - Geografia Fisica E GeomorfologiaGully erosionArea under the roc curveSettore GEO/05 - Geologia ApplicataGeology
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Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios

2019

Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morph…

Watershedlcsh:Hydraulic engineering010504 meteorology & atmospheric sciencesCalibration (statistics)Settore GEO/04 - Geografia Fisica E GeomorfologiaGeography Planning and Development0207 environmental engineering02 engineering and technologyGully erosionrobustnessAquatic Science01 natural sciencesBiochemistrygislcsh:Water supply for domestic and industrial purposeslcsh:TC1-978Statisticsgully erosion susceptibility020701 environmental engineering0105 earth and related environmental sciencesWater Science and TechnologyMathematicslcsh:TD201-500Multivariate adaptive regression splinesReceiver operating characteristicMars Exploration Programmars algorithmSample size determinationSettore GEO/05 - Geologia ApplicataKappaWater
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Evaluation of multi-hazard map produced using MaxEnt machine learning technique.

2021

Abstract Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gul…

Environmental sciencesNatural hazardLandslideGully erosionFloodingScienceQRNatural hazardsMedicineMaxentIranArticleScientific reports
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