The aim of this study was to determine how well the landslide susceptibility parameters,obtained by data-dependent statistical models,matched with the parameters used in the literature.In order to achieve this goal,20...The aim of this study was to determine how well the landslide susceptibility parameters,obtained by data-dependent statistical models,matched with the parameters used in the literature.In order to achieve this goal,20 different environmental parameters were mapped in a well-studied landslide-prone area,the Asarsuyu catchment in northwest Turkey.A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database.In order to run a series of logistic regression models,different random landslide-free sample sets were produced and combined with seed cells.Different susceptibility maps were created with an average success rate of nearly 80%.The coherence among the models showed spatial correlations greater than 90%.Models converged in the parameter selection peculiarly,in that the same nine of 20 were chosen by different logistic regression models.Among these nine parameters,lithology,geological structure(distance/density),landcover-landuse,and slope angle were common parameters selected by both the regression models and literature.Accuracy assessment of the logistic models was assessed by absolute methods.All models were field checked with the landslides resulting from the 12 November 1999,Kaynas¸li Earthquake(Ms7.2).展开更多
A municipal solid waste(MSW)management system needs solid waste management(SWM)techniques where the presence of a sanitary landfill is vital.One of the most important issues of sanitary landfilling is to locate the f...A municipal solid waste(MSW)management system needs solid waste management(SWM)techniques where the presence of a sanitary landfill is vital.One of the most important issues of sanitary landfilling is to locate the facility to an optimal location.Despite the versatility and case-dependent nature of conventional expert-based site selection procedures,the number of sites to be chosen increases with increased population forcing a number of constraints.Consequently,constraints and environmental regulations mechanically mask unsuitable areas,leaving very little areas to be assessed.This turns the situation into a challenging issue for a geographical information system(GIS)used with multicriteria decision analysis(MCDA),to select optimal site.The study aims to apply MCDA integrated with GIS to select possible sites of a MSW landfill with the same expert and same cognitive parameters while compared with the already present one.Results of this study revealed that conventional expert-based methods could not always evaluate all constraints at the same time and map reproduction is limited when parameter maps are changing rapidly in time.In order to produce cognitive and reproducible analyses,GIS with MCDA integration offers a good solution for site selection issue and forms a good alternative for conventional methods.展开更多
文摘The aim of this study was to determine how well the landslide susceptibility parameters,obtained by data-dependent statistical models,matched with the parameters used in the literature.In order to achieve this goal,20 different environmental parameters were mapped in a well-studied landslide-prone area,the Asarsuyu catchment in northwest Turkey.A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database.In order to run a series of logistic regression models,different random landslide-free sample sets were produced and combined with seed cells.Different susceptibility maps were created with an average success rate of nearly 80%.The coherence among the models showed spatial correlations greater than 90%.Models converged in the parameter selection peculiarly,in that the same nine of 20 were chosen by different logistic regression models.Among these nine parameters,lithology,geological structure(distance/density),landcover-landuse,and slope angle were common parameters selected by both the regression models and literature.Accuracy assessment of the logistic models was assessed by absolute methods.All models were field checked with the landslides resulting from the 12 November 1999,Kaynas¸li Earthquake(Ms7.2).
基金Scientific&Technological Research Council of Turkey(TUBI˙TAK)for providing financial support of this work under Grant No:106Y305.
文摘A municipal solid waste(MSW)management system needs solid waste management(SWM)techniques where the presence of a sanitary landfill is vital.One of the most important issues of sanitary landfilling is to locate the facility to an optimal location.Despite the versatility and case-dependent nature of conventional expert-based site selection procedures,the number of sites to be chosen increases with increased population forcing a number of constraints.Consequently,constraints and environmental regulations mechanically mask unsuitable areas,leaving very little areas to be assessed.This turns the situation into a challenging issue for a geographical information system(GIS)used with multicriteria decision analysis(MCDA),to select optimal site.The study aims to apply MCDA integrated with GIS to select possible sites of a MSW landfill with the same expert and same cognitive parameters while compared with the already present one.Results of this study revealed that conventional expert-based methods could not always evaluate all constraints at the same time and map reproduction is limited when parameter maps are changing rapidly in time.In order to produce cognitive and reproducible analyses,GIS with MCDA integration offers a good solution for site selection issue and forms a good alternative for conventional methods.
