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Volume 1, Issue 1 (7-2003)
Abstract
(Paper pages 24-41 ) Different methods are used for landslide hazard zonation. Some of the methods are based on specific condition of the area. In this research, applicapibility of a number of landslide hazard zonation methods in Latian Dam watershed is evaluated. For this purpose Latian Dam watershed due to variety in geological condition, engineering geological characteristics, topography, geomorphology, and precipitation was selected. Different thematic layers including geology, distance from active faults, elevation, slope rate and aspect, precipitation, and vegetation cover were prepared. More than 150 single and landslide zones were recorded based on aerial photo interpretation and field survey. The data were analyzed to find out about landslide controlling factors. Considering instability controlling factors, Nilsen, Information Value, Weight of evidence, and Density area methods were used for preparation of landslide hazard zonation in the watershed. The comparison of the prepared hazard zonation maps with landslide inventory map indicates that weight of evidence and information value methods with accuracy of 99.4 and 99.7 percent respectively are most appropriate methods for preparation of landslide hazaed zonation map in similar area in Central Alborz.
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Volume 6, Issue 1 (11-2012)
Abstract
Prediction of location of future earthquakes with event probability is useful in reduction of earthquake hazard. Determination of predicted locations has attracted more attention to design, seismic rehabilitation and reliability of structures in these sites. Many theories were proposed in the prediction of time of occurrence of earthquake. There is not a method for prediction time of future earthquakes. Many studies have been done in the prediction of magnitude of earthquakes, but there are not any investigations on prediction of earthquake hazard zonation. In this study, the locations that have probability of the event of future earthquake have been predicted by artificial neural networks in Qum and Semnan. Neural networks used in this study can extract to complicate properties of patterns by receipting the interval patterns. Furthermore, the map of earthquake hazard zonation has been drawn. Properties of occurred earthquake were collected since 1903. The most probable event of earthquake in Qum has been predicted 31.6% in center, and 28.9% in north of Semnan
