0000000000965508

AUTHOR

Omid Rahmati

0000-0001-5672-8525

showing 2 related works from this author

PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches

2019

Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system, which includes both the cutoff-dependent and –independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the fir…

Performance analysiEnvironmental EngineeringGeospatial analysis010504 meteorology & atmospheric sciencesComputer scienceSettore GEO/04 - Geografia Fisica E GeomorfologiaComputationGoodness-of-fit010501 environmental sciencescomputer.software_genre01 natural sciencesRobustness (computer science)ValidationEnvironmental ChemistryWaste Management and Disposal0105 earth and related environmental sciencescomputer.programming_languageEnvironmental modellingReceiver operating characteristicSpatial modellingPerformance analysisLandslidePMTPython (programming language)22/4 OA procedurePollutionDrought riskITC-ISI-JOURNAL-ARTICLEData miningPredictive model evaluation frameworkcomputerScience of The Total Environment
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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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