F Kamranzad; E Mohasel Afshar; M Mojarab; H Memarian
Abstract
Landslide is one of the natural phenomena which can cause catastrophic losses or damages in life and property each year. Hence, it is very important to recognize landslide-prone areas and apply methods to prevent or reduce slope instabilities and landslide hazard and risk. For this purpose, landslide ...
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Landslide is one of the natural phenomena which can cause catastrophic losses or damages in life and property each year. Hence, it is very important to recognize landslide-prone areas and apply methods to prevent or reduce slope instabilities and landslide hazard and risk. For this purpose, landslide hazard zonation is one of the indirect and efficient methods. This study aims to apply data-driven and AHP methods to provide a zonation map of landslide hazard potential in the Tehranprovince of Iran. First, six essential and available factors including slope, slope direction, geologic background, distance from faults, earthquake acceleration and rainfall were selected to be classified in GIS based on engineering judgment. By superposing data layers over landslide distribution map in data-driven method and expert judgment in AHP method, layers and sub-layers were weighted and combined. The landslide-hazard zonation map was then produced for each of the methods in GIS. Results showed that in data-driven method 92.9% of landslides fall into the perilous zone (i.e. hazardous and very hazardous zones) having an area of 7135.15 km2, which is 37.2% of total area of Tehran province. For the AHP method, 96.47% of the landslides were in perilous zone with an area of 10344.7 km2, which is 53.9% of the total area of the province. Finally, the ratio of percentage of landslides in the perilous zone to the percentage of total area of the zone was calculated. The ratio is 2.5 for the data-driven and 1.79 for the AHP method. The larger ratio in the data-driven method indicates its better consistency than the AHP method, implying more coverage of landslides in a smaller perilous area by the data-driven method. This result represents better accuracy of the data-driven method than the AHP method in landslide hazard zonation.
E Ghadiri-Sufi; M Yousefi
Abstract
Integration of different kinds of data is a useful method which can be used in exploration studies to determine the location of undiscovered hidden or outcropping mineral deposits in an area under prospecting. The results obtained by considering all data sources and their relations have better reliability. ...
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Integration of different kinds of data is a useful method which can be used in exploration studies to determine the location of undiscovered hidden or outcropping mineral deposits in an area under prospecting. The results obtained by considering all data sources and their relations have better reliability. In this regard, modeling the mineral potential is commonly used to combine the results achieved by different exploration methods in order to generate target areas. In this research, surface exploration data over the 1:100000 geological map of the Manheshan quadrangle (Zanjan Province) were integrated by a new data-driven and knowledge-driven fuzzy approach to determine areas of high mineralization potential. Various dataset used in this study include geological map, geochemical stream sediment data, and fault distribution map. In this new approach, evidential geochemical and fault density maps were weighted ad produced without the use of any analyst’s subjective judgment and location of known indices. In contrast, the evidential weighted geological map was produced considering the analyst’s subjective judgment. The weighted data layers produced by fuzzy logic were then integrated using OR and Gamma fuzzy logic operators. Finally, known mineral occurrences (Zn-Pb) in the Mahneshan area were used to evaluate the generated models. Results show that the generated target areas have a good spatial coincidence with the position of known mineral occurrences.
