Showing 8 results for Landslide
H Pourghasemi, H Moradi, M Mohammadi, M Mahdavifar,
Volume 12, Issue 46 (1-2009)
Abstract
One of our first activities in natural resources management and development programs is to acquire knowledge on Landslide Susceptible areas. The aim of this research is landslide hazard zonation in some part of Haraz watershed between Vana village and Emam zadeh Ali, using fuzzy membership functions and fuzzy operators. At first landslide points were recognized using arial photography and field studies. Afterwards, the inventory map of landslide was prepared. Then, each effective element in landslide such as: slope, aspect, elevation, lithology, landuse, distance of road, distance of drainage, distance of fault and precipitation map was prepared in GIS environment.These data were saved in raster and vector format in ILWIS software and used for analysis with theory of fuzzy sets. Fuzzy analysis was made by IDRISI software, after assigning value and fuzzy membership functions. In this research we used different fuzzy operators such as (And, Or, Sum, Product and Gamma). Results showed Gamma fuzzy operator had the best accuracy ( ) in making landslide susceptibility map in study area.
S. Z. Mosavi Khatir, A. Kavian, A. K. Soleimani,
Volume 14, Issue 53 (10-2010)
Abstract
In this research, logistic regression analysis was used to create a landslide hazard map for Sajaroud basin. At first, an inventory map of 95 landslides was used to preduce a dependent variable, which takes a value of 0 for absence and 1 for presence of landslides. Ten factors affecting landslide occurence such as elevation , slope gradient, slope aspect, slope curvature, rainfall, distance from fault, distance from drainage, distance from road , land use and geology were taken as independent parameters. The effect of each parameter on landslide occurrence was determined from the corresponding coefficient that appears in the logistic regression function. The interpretation of the coefficients showed that road network plays the most important role in determining landslide occurrence. Elevation, curvature, rainfall and distance from fault were excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. After transferring final probability function into Arc/view 3.2 software, landslide susceptibility map was prepared. The results of accuracy assessment showed that overall accuracy of produced map is 85.3 percent. Therefore, 53% of the area was located in very low hazard, 18.3% in low hazard, 21% in moderate hazard and 7.7 % residual area is located in high hazard regions. Model and then susceptibility map verity was assessed using -2LL, Cox and Snell R2, Nagelkerk R2, and was validated.
H. R. Pourghasemi, H. R. Moradi, M. Mohammdi, R. Mostafazadeh, A. Goli Jirandeh,
Volume 16, Issue 62 (3-2013)
Abstract
The aim of present research is landslide hazard zoning using Bayesian theory in a part of Golestan province. For this purpose, landslides inventory map was created by landslide locations of landslide database (392 landslide locations). Then, the maps of effective parameters in landslide such as slope degree, aspect, altitude, slope curvature, geology, land use, distance of drainage, distance of road, distance of fault, stream power index (SPI), sediment transport index (STI), and rainfall were prepared in GIS environment. Relationship between effective factors and landslide locations were considered using Bayesian probability theory. In the next step, parameters classes weights were found and the landslide susceptibility mapping was achieved by fourteen modeling approaches (using whole parameters and deleting parameters one by one). The verification results by ROC curve and 30% landslide locations showed that the Bayesian probability model has 71.37% accuracy for the second approach of modeling in the study area.
H.r. Pourghasemi, H.r. Moradi, S.m. Fatemi Aghda,
Volume 18, Issue 70 (3-2015)
Abstract
The objective of the current research was to prioritize effective factors in landslide occurrence and its susceptibility zonation using Shannon’s entropy index in North of Tehran metropolitan. To this end, 528 landslide locations were identified using satellite images such as Geoeye (2011-2012), SPOT-5 (2010), and field surveys, and then landslide inventory map was created for the study area in ArcGIS environment. Data layers such as slope degree, slope aspect, plan curvature, altitude, lithology, land use, distance of road, distance of fault, distance of drainage, drainage density, road density, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), surface area ratio (SAR) and topographic position index (TPI) were created and the mentioned maps were digitized in GIS environment. Prioritization of effective factors by Shannon’s entropy index showed that the layers such as land use, lithology, slope degree, stream power index, and NDVI had the most effect on landslide occurrence. However, factors of topographic position index and plan curvature had the least effect. Also, landslide susceptibility zoning by the mentioned model and its accuracy assessment using relative operating characteristics (ROC) curve and 30 percent of landslide locations showed an accuracy of 82.83% with a standard error of 0.0233 in the study area.
K. Shirani, A. R. Arabameri,
Volume 19, Issue 72 (8-2015)
Abstract
This research was conducted to prepare landslide susceptibility zonation (LSZ) map for the Dez-e-Ouliabasin using logistic regression model. For this purpose, at first, the most important factors affecting land sliding including slope, aspect, elevation, precipitation, the distance from road, the distance from fault, the distance from drainage, land use, and lithology were determined. Then, thelandslide inventory mapwas preparedby using field digital checks, GPS and satellite images. In the next step, the landslide susceptibility zonation mapwas preparedby usinglogistic regression method. According to the obtained coefficients for LSZ maps, the most important factor in the study area was elevation layer. The Receiver Operating Curve (ROC) index value was calculated (0.92), which indicates a very high level and suggests thatthe observed mass movements have a strong relationship with the logistic regression model.
K. Shirani,
Volume 21, Issue 1 (6-2017)
Abstract
Landslide susceptibility mapping is essential for land use planning and decision-making especially in the mountainous areas. The main objective of present research is to produce landslide hazard zonation at Sarkhoun basin in Karoon basin using two statistical models such as an index of Shannon’s entropy and weight of evidence and to assess the obtained results. At the first stage, landslide locations were identified in the study area by interpretation of aerial photographs, image sattellites, and from field investigations and then landslide inventory map was created for study area. The landslide conditioning factors such as slope degree, slope aspect, altitude, lithology, land use,distance of road, distance of fault, distance of drainage, topographic wetness index (TWI), Convergence Index, and precipitation were extracted from the spatial database and they were digitized in GIS environment. With integrated variables, landslides were calculated in each variable class and weighted in index of entropy and weight of evidence model. In the last, landslide hazard zonation map were obtained with both of models. The results of landslide susceptibility mapps of both statistical models were indicated more than 70 percent the occurred landslides were located in very high and high zones that about half of the basin area (over 45 percent) constitute. Also, the results of both models together were revealed that land use, has the greatest impact on the occurred landslides. Resolution of the zones, based on the seed cell area index (SCAI) and frequency ratio (FR) were evaluated suitable for both statistical models. Finally, the ROC (receiver operating characteristic) curves for landslide susceptibility maps were drawn and the areas under the curve (AUC) were calculated. The verification results of both models were evaluated very well and showed that the index of entropy model (AUC=89%) performed slightly better than weight of evidence model (AUC=82%).
K. Shirani,
Volume 22, Issue 3 (11-2018)
Abstract
The persistent scattering interferometry (PSI) technique is a valuable tool in displacements' monitoring of earth's surface. The persistent scattering interferometry (PSI) based on persistent scatterrer (PSInSAR) is one of the techniques used to reduce constraints (temporal and spatial incoherency). It is based on persistent scatterer and monitor displacement of only the pixels with timely-constant properties of scatterer. In order to detect and monitor landslides, two time series SAR data sets including PALSAR ascending images from 2007 to 2010 and ASAR images from 2003 to 2010 with C-band and L-band wavelength were applied, respectively. Also, the PSI technique was implemented in a landslide near Noghol village, Padena, Semirom of Isfahan province. The results revealed that both PALSAR and ASAR data set were efficient in identifying Noghol landslide. The results obtained from ASAR and PALSAR images processing (with the values of 1253 mm and 1578 mm in two stages of time 4 and 7 years, respctively) were compared. The obtained vertical displacement's rate of the landslide by using ASAR data was more suitable because of its descending orbit. However, PALSAR images that indetified more persistent scatterrer points were better in the detection of the landslide area. The results of GPS and PSInSAR techniques revealed that landslide displacement values and aspect were the same, confirming 135 centimeters of displacement to the west aspect. Finally, a combination of radar data in two different passes provided the possibility of monitoring the mechanism of landslide and its movement direction.
K. Nosrati, M. Heydari, M. Hoseinzadeh, S. Emadoddin,
Volume 22, Issue 3 (11-2018)
Abstract
Ziarat drainage basin, in the southern part of Gorgan city, is exposed to mass movement, especially landslide occurrence, due to geologic, geomorphologic, and anthropogenic reasons. The objectives of this study were to predict landslide susceptibility and to analyze the effective factors using rare events logistic regression. In view of this, the map layers of the variables including geology, land use, slope, slope aspect, distance of road, distance of fault and distance of river were prepared using topographic and geologic maps and aerial photo interpretation. In addition, the map layers of the soil variables including the percent of clay, silt, sand, and saturation water as well as plasticity limit index were determined based on the laboratory analysis of 32 soil samples collected from landslide sites and 32 soil samples obtained from non-occurrence landslide sites. The controlling factors of landslide were determined using rare events logistic regression analysis; then based on their coefficients, the landslide risk zoning map was prepared and validated. The landslide risk zoning map was classified in five different hazard classes ranging from very low risk to very high risk; the very high risk class with 16.8 km2 was assigned as the having the highest percent of the catchment area. The results of the model validation showed that the rare events logistic regression model with the receiver operating characteristic (ROC) of 0.69 could be a suitable prediction model for the study area. The results of this study could be, therefore, useful for corrective actions and watershed management landslide high-risk zones.
