A. H. Pasha; A. Sorbi; S. Behzadi
Abstract
Mass movements, especially landslides, are one of the natural hazards that to a large extent occur, are controlled, or are prevented by human. It is obvious that human interferences in nature regardless of stability conditions and its natural balance leads to physical reactions from the environment to ...
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Mass movements, especially landslides, are one of the natural hazards that to a large extent occur, are controlled, or are prevented by human. It is obvious that human interferences in nature regardless of stability conditions and its natural balance leads to physical reactions from the environment to return the sustainability and balance. Damages caused by the landslides, which have been growing in recent decades, have made humans to find appropriate solutions to reduce and control this phenomenon. Zonation of areas susceptible to landslide is one of the most widely used methods to avoid hazardous areas or applying controlling methods in hazardous areas. This research uses artificial neural network for zonation of landslide susceptibility in the Qazvin-Rasht quadrangle area. The studied area is one of the most susceptible areas for landslide event in terms of topography, climate, and geology, as the history of the area shows 338 recorded landslides. Fifteen variables studied in other researches as effective variables in occurrence of landslides were selected to investigate this area. By combining these variables and the map of existing landslides, value of each of the 15 variables was extracted for sliding points. In the next stage, a number of points (1000 points) were randomly selected from the area and values of these variables were extracted for them. Each of the two data sets was divided into two training (70%) and test (30%) categories. We combined each of the two training and test categories, and used their output for training and testing the network. The number of internal layers of the neural network was determined to be 9 layers based on trial and error method and calculation of the root mean square error value (RMSE = 0.4041). The constructed neural network is of feedforward networks type with back-propagation algorithm and its training algorithm is of Levenberg-Marquardt back-propagation training algorithm type. After training and testing the network and conducting necessary corrections on it, the constructed neural network was used to predict the sensitivity of landslides in studied area. We placed results of this prediction in a range from 0 to 1 and obtain the best zonation map of the landslide susceptibility by choosing a threshold. Final evaluation of the zonation map of landslide susceptibility in the Qazvin-Rasht quadrangle shows an error of approximately RMSE = 0.4164 and the constructed neural network identifies 298 out of 338 occurred landslides in the high-risk zone, indicating the accuracy of 88.1%.
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.
R Ramazani Omali; N Hafezi Moghaddas; K Heidari
Abstract
Rock falls are the usual forms of slope instability in hill slopes. The high velocity and rapid occurrence are the main differences of rock fall and other rock instability. Therefore, the rock falls are among the most destructive mass movements and results in high loss of lives and heavy damage to the ...
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Rock falls are the usual forms of slope instability in hill slopes. The high velocity and rapid occurrence are the main differences of rock fall and other rock instability. Therefore, the rock falls are among the most destructive mass movements and results in high loss of lives and heavy damage to the structures, roads, residential area, farms and etc. In this research, rock fall in rocky slopes of the TepalMountain in northwest of Shahrood city was investigated. For this, the large scale geological map of area (scale of 1:10000) was produced and joint studies in 12 sections performed. Then, the slope instability was analyzed by two methods of stereographic (using Dips 5.103 software) and analytical method (by Swedge 4.078 and rock fall 4.039 softwares). The results show that all of the slopes are stable in static conditions and become instable in dynamic state. In critical states of huge raining and earthquake intense instability will be occurred and the big problems could be created for the down area of slopes. Based on the results of analytical and using the Arc GIS 9.3 rock fall hazard zonation map was produced. In addition, by overlaying of landslide hazard map and land use map, the area affected by rock falls was distinguished. The results of this study show that Salamaty road, Mazar Shohaday Gomnam and its access road, some part of AzadiPark, the tourism hotel, some of the residential area down of the Salamaty road and some parts of the AbsharPark are located in the hazard zones.
M Abasi; S Bagheri Saidshokri; M Jafari Aghdam
Abstract
The Noa Anticline is located in west part of KermanshahProvince and due to extension of limestone formations, existing of faults and joints, and also climatic conditions of the area involves an evolved karst. This study carried out to recognize the karstic evolution process and zonation as well as the ...
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The Noa Anticline is located in west part of KermanshahProvince and due to extension of limestone formations, existing of faults and joints, and also climatic conditions of the area involves an evolved karst. This study carried out to recognize the karstic evolution process and zonation as well as the impact of different factors on the process of karst developing in the Noa anticline. The data gathered for this study are topographic, geological, soil, land use, aerial, and satellite maps and also climatological statistic data. In this study, nine environmental factors as independent parameters and karst features geomorphology as a dependent parameter were examined. Then, with intensive field works, we recognized the closed holes as the most evolutionary ones of karst features of this area, and concerning the characteristics of these landforms, a proper weight was given to each parameter. Finally, by using GIS software, we prepared overlap maps and in a final manner using Entropy model, adaptation of factors and definitive modification have been performed. The results of this study show that this area embraces four categories of Karsts include lack of karst evolution, moderate evolution, high evolution and very high evolution categories. Regarding nine factors, five factors such as distance from fault, slope, slope aspect, temperature and rainfall recognized as the most effective ones and other factors such as soil factor, land use, contour lines and lithology as with no effect on area’s karst is recognized. The model preciseness applying on the closed holes shows that there are 92 % closed holes in the two categories (high evolution and very high evolution) and have indicated the favorite effectiveness of using Entropy model on the karst evolution zonation.
K. Shirani; A. Seif
Abstract
The landslide hazard zonation was executed by different methods and many of these methods were based on special condition of the study area. This research, at first, Pishkuh region (fereyidonshahr administration) was selected with 77646 hectars area in west of Esfahan province. Then, landslide inventory ...
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The landslide hazard zonation was executed by different methods and many of these methods were based on special condition of the study area. This research, at first, Pishkuh region (fereyidonshahr administration) was selected with 77646 hectars area in west of Esfahan province. Then, landslide inventory map was obtained by using airial photos, satellite images (ETM+2002), geology maps and surveying of the field. Also, the 8 important factors are effective in occurrence of landslide including slope gradient, aspect, lithology, landuse, rainfall, and distance to fault, road and drainage were determined by using inspect of feild, literature review in similar regions of northern Karoon and Dez basins in scale of 1:50000. In order to increasing of precision, speed and facility of analysis, all of the attribute and spatial data were entered into ArcGIS software. After producing of information layers and weighting to effective factors by using inventory map, landslide hazard zonation was created by two bivariarte statistical methods including to Information value and Density area methods and the results were assessed. When the density ratios (Dr) Index (for the purpose of camparing between hazard classes) increase in each two method, then hazard rate will increase and the separation between hazard zone classes is acceptable and increasable. The quality sum (Qs) and precision (P) indices (in order to comparing of methods together) for Information value method are 0.65, 0.034 and for Density area method are 0.56, 0.028, respectively. It is clear that the information value is better than Density area in landslide hazard zonation.
