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This paper describes the application of a well-known multi-criteria decision-making technique, called preference ranking organization method for enrichment evaluation (PROMETHEE II), in combination with fuzzy analytical hierarchy process (FAHP), as a weighting technique to explore landslide susceptibility mapping (LSM). To this end, eight landslide-related geodata layers of the Minoo Dasht located in the Gorgan province of Iran, involving slope, aspect, distance to river, drainage density, distance to fault, mean annual rainfall, distance to road and lithology have been integrated using the PROMETHEE II enhanced by FAHP technique. Afterward, the receiver operating characteristics (ROC) curves for the proposed LSM were drawn using an inventory of landslides containing 83 recent and historic landslide points, and the area under curve = 0.752 value was calculated accordingly. Additionally, to further verify the practicality of such susceptibility map, it was also evaluated against the landslide inventory using simple overlay. The outcome was that about 11 % of the occurred landslide points fall into the very high susceptibility class of the LSM, but approximately 52 % of them indeed fall into the high and very high susceptibility zones together. Also, it resulted that no recorded landslide occurred in the zone of very low susceptibility. According to the results of the ROC curves analysis and simple overlay evaluation, the produced map has exhibited good performance. 相似文献
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Hong Haoyuan Shahabi Himan Shirzadi Ataollah Chen Wei Chapi Kamran Ahmad Baharin Bin Roodposhti Majid Shadman Yari Hesar Arastoo Tian Yingying Tien Bui Dieu 《Natural Hazards》2019,96(1):173-212
Natural Hazards - The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of... 相似文献
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Saeed Rahimi Majid Shadman Roodposhti Rahim Ali Abbaspour 《Environmental Earth Sciences》2014,72(6):1979-1992
Flood spreading is one of the suitable strategies to control and benefit from floods which in turn improve the groundwater recharge, makes soil more fertile, and increases nutrients in soil. It is also a method for reusing sediment, which is usually wasted. Thus, selection of suitable areas for flood spreading and directing the flood water into permeable formations are amongst the most effective strategies in flood spreading projects. Having combined analytic hierarchy process (AHP) of multi-criteria decision analysis and genetic algorithm (GA) of artificial intelligence approaches, this paper addresses the problem of finding the most suitable area location for flood spreading operation in the Gareh Bygone Plain of Iran. To this end, the nine effective geodata layers including slope, alluvium thickness, geology, morphology, electrical conductivity, land use, drainage density, aquifer transmissivity, and elevation were prepared in geographic information system environment. This stage was followed by elimination of the exclusionary areas for flood spreading while determining the potentially suitable ones. Having closely examined the potentially suitable areas using the proposed methodology, the land suitability map for flood spreading was produced. The AHP and GA were used for ranking all the alternatives and weighting the criteria involved, respectively. The results of the study showed that most suitable areas for the artificial groundwater recharge are located in Quaternary Qft 2 and Qsf geologic units and in morphological units of pediment and Alluvial fans with slopes not exceeding 2 %. Finally, further evidence for the acceptable efficiency of the integrated AHP–GA method in locating most suitable flood spreading areas have been provided by such significant spatial coincidence between the produced map and the control areas located near Kowsar research station, where the earlier flood spreading projects were successfully performed. 相似文献
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Bakhtiar Feizizadeh Majid Shadman Roodposhti Thomas Blaschke Jagannath Aryal 《Arabian Journal of Geosciences》2017,10(5):122
This study compares the predictive performance of GIS-based landslide susceptibility mapping (LSM) using four different kernel functions in support vector machines (SVMs). Nine possible causal criteria were considered based on earlier similar studies for an area in the eastern part of the Khuzestan province of southern Iran. Different models and the resulting landslide susceptibility maps were created using information on known landslide events from a landslide inventory dataset. The models were trained using landslide inventory dataset. A two-step accuracy assessment was implemented to validate the results and to compare the capability of each function. The radial basis function was identified as the most efficient kernel function for LSM with the resulting landslide susceptibility map showing the highest predictive accuracy, followed by the polynomial kernel function. According to the obtained results, it concluded that using SVMs can generally be considered to be an effective method for LSM while it demands careful consideration of kernel function. The results of the present research will also assist other researchers to select the best SVM kernel function to use for LSM. 相似文献
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