mojtaba rafyi; Khalil Rezaei; KOUROSH SHIRANI; MOHAMADI NASRIN
Abstract
Identification of areas that prone to subsidence and estimation of its rate plays an important role in the control management of this phenomenon. Differential interferometry radar technique (D-InSAR) with very high accuracy is one of the most suitable ways for identify and measure the rate of subsidence. ...
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Identification of areas that prone to subsidence and estimation of its rate plays an important role in the control management of this phenomenon. Differential interferometry radar technique (D-InSAR) with very high accuracy is one of the most suitable ways for identify and measure the rate of subsidence. In this study, to identify and measure the subsidence in Mahyar Plain differential radar interferometry techniques have been used in the period of 2004 to 2010. For this purpose, eight pair images of time series were used from ASAR sensor in C-band radar in ascending passage. The method used in this study is based on laboratory-field surveys. For validation of technique, survey data such land use and geology maps and data of observation wells in the region were used. As a result, maximum rate of annual subsidence in the area was 6.4 cm yr-1. Also, results showed that the highest amount of subsidence occurred in areas under cultivation and due to excess extraction of groundwater and subsidence of aquifer surface. The rate of subsidence was obtained 0.384 cm for each two cm drop of water table according to the relationship between subsidence and the changes of piezometric wells surface.
Alireza Arabameri; Kalil Rezaei; Mohammadhossein Ramshet; Kourosh Shirani
Abstract
Landslide susceptibility and its risk assessment is the main part of landslide risk mapping. In this study, landslide susceptibility of Oliya's Padena in Semirom is mapped using artificial neural network. A total of 23 factors in relation to landslide in the region were initially characterized. The spatial ...
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Landslide susceptibility and its risk assessment is the main part of landslide risk mapping. In this study, landslide susceptibility of Oliya's Padena in Semirom is mapped using artificial neural network. A total of 23 factors in relation to landslide in the region were initially characterized. The spatial location of landslide events was then determined by field study as well as aerial photo analysis. AHP analysis tends to 14 out of 23 parameters as the important factors for further steps. A total of 72 (70%) and 31 (30%) out of 103 detected landslide events in the study area were selected as training and validation data for neural network analysis, respectively. A multilayer perceptron back propagation algorithm with sigmoid as activation function was developed. The best topology was determined by using conventional criteria including mean square error, root mean square error, maximum absolute error and correlation coefficient. Results show that a 14-4-1 array is the optimum topology for landslide susceptibility zoning in the region. The weight of each input layer was estimated by frequency ratio. In order to map landslide, ROC graph and area under curve indices were used and the accuracy of output map was computed. Results from validation shows that area under curve for the obtained model is about 0.938 (93.8%) that is considered as high resolution prediction group. According to this study, a total of 29.61 square kilometers (93.25%) of the landslide areas is categorized in very high and high susceptible groups.