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A destructive landslide occurred in spring 2019, in Hosein Abad Kalpush Village, Northeast of Iran, after an unusually rainy period. Significant damages were observed in buildings and roads located on the slope failure zone. This paper represents a preliminary reconnaissance report prepared through the site visit done by the authors, a few days following the landslide occurrence. Then, the geotechnical and geophysical properties of the sliding area are reported using the site investigation tests. Subsequently, the rainfall data are incorporated for numerical slope stability analyses. This study shows the variation of the failure mechanism of the slope before and after rainfall. The results confirm that the factor of safety decreases 1.78 times after a 3-month period of rain with rainfall intensity about 6 times the long-term average. Finally, the performance of two slope stability improvement strategies is evaluated to mitigate the landslide hazard. The results of the study can provide a perspective on the climate change impacts in Iran for future geosystem designs, geohazard prediction, and the selection of rainfall-induced landslide mitigation strategies. [ABSTRACT FROM AUTHOR]

As a frequent natural disaster, landslides incur significant economic and human losses worldwide. The main idea of this paper is to propose novel integrative models for landslide susceptibility evaluation in a prone area of Chaharmahal va Bakhtiari Province in Iran. To do this, four metaheuristic techniques, namely chimp optimization algorithm (ChOA), crow search algorithm (CSA), satin bowerbird optimization (SBO), and water cycle algorithm (WCA) are used to supervise the training of an artificial neural network (ANN). A spatial database is created by 170 historical landslides and 14 most common conditioning factors. The models are optimized in terms of hyper-parameters and elite ones are then used to produce the susceptibility maps. During the training phase, all models could acquire a reliable understanding of the landslide pattern. The obtained accuracy index of area under the receiving operating characteristic curve (AUROC) showed that the maps produced by the WCA-ANN (AUROC = 0.925) and SBO-ANN (AUROC = 0.900) are more accurate than those of the ChOA-ANN (AUROC = 0.851) and CSA-ANN (AUROC = 0.855). Considering this assessment, the suggested hybrid models are reliable enough for landslide susceptibility modeling. Accordingly, the generated susceptibility map can be used for urban planning and generating alert systems over the studied area. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners. [ABSTRACT FROM AUTHOR]

The right abutment of Abnahr dam consists of Cretaceous marl and some marly limestone that are highly fractured due to tectonic stresses. Low strength and highly fractured structure of the rocks result in fragmentation during drilling by the rock sampling tools. However, this material is still too strong for conventional soil sampling methods. The types of marl are similar to rock material in natural landscapes but drilled material has soil-like behavior. This paper evaluates the behavior of this material in slope stability problems that concern the right abutment of Abnahr dam in existing conditions. For attaining reliable answers to this problem, several undisturbed samples were provided with assistance of the experienced experts and using specific methods. As these samples were rather soils, they were tested by various soil tests such as three-dimensional tests. Moreover some samples were gathered by hand-operated methods including test pits. In order to gain information about the rock parameters, several rock tests were performed. In the next stage, by use of the results of laboratory tests and field observations, slope stability analyses were performed on several models of right abutment of the dam. Lastly, a comparison between different analysis methods of the performed models on right abutment under virtual loads led to important conclusions. [ABSTRACT FROM AUTHOR]

Latian dam is located in the North East of Tehran in Elburz Mountain. It falls in the category of large dams according to the International Committee on Large Dams (ICOLD). It was constructed in 1967 for agricultural purposes, drinking water, and power generation. Producing triggered earthquakes may be a consequent result of dam construction. In this paper, the complete seismic statistics of the region from 1996 onwards has been studied to understand the seismic condition of Latain region. For this purpose, frequency of earthquakes within a radius of 30/60 km around the dam is studied considering its relationship with the reservoir volume variation. Using Gutenberg-Richter rule, parameter b of the region was determined within the same region. The results of this study show the existence of triggered seismicity around the reservoir of Latian dam. Considering the tectonic-geological condition of the region, the existence of triggered earthquakes may create landslides in the reservoir and around it. [ABSTRACT FROM AUTHOR]

Most of the recent earthquakes in Iran were associated with some types of geological hazards that were the cause of additional damage and casualties in the earthquake-affected areas. In order to reduce the impacts of geo-hazards, several policies and plans were prepared, approved, and implemented by the Iranian government during the last two decades. However, such activities have not yet resulted in risk reduction to an acceptable level; since they are not formulated based on local conditions and are not linked to location-specific comprehensive plans. In this paper, after introducing some of the impacts of geo-hazards associated with recent earthquakes in Iran, the country trends in development of relevant risk reduction plans and policies are introduced, evaluated, and compared with some other countries having similar challenges. Strategies toward risk mitigation in the country are addressed, and a number of indices for the assessment of the geo-hazard risk reduction plans and activities are introduced. Finally, a conceptual model for the evaluation of the preparedness level against geo-hazards is proposed and discussed. [ABSTRACT FROM AUTHOR]

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations. [ABSTRACT FROM AUTHOR]

This work demonstrates the potential of synthetic aperture radar interferometry (InSAR) based on interferometric point target analysis (IPTA) approach for landslide-induced deformation measurement. Twenty-eight Sentinel-1A images acquired between October 14, 2014, and October 27, 2016, are employed in order to measure the surface deformation associated with Kahroud landslide, northern Iran. Due to the lack of geodetic measurements and thanks to capability of processing small baseline highly coherent interferograms in the study area, the conventional small baseline subset (SBAS) time series analysis is exploited for investigation of the IPTA performance. The cross-comparison indicates some unwrapping errors in the IPTA results and removal of a large number of point target candidates due to the remained nuisance terms in the phase measurements, the deviation of the deformation behavior from a simple linear model employed by IPTA and the temporal gap in the available dataset. Two different sliding mechanisms, however, are revealed based on the SBAS time series analysis results. In the second part of the research, the slope stability analysis to characterize the slope dynamics is finally performed based on the deformation field extracted from two ascending and descending single interferograms. Accordingly, the soil shear strength parameters as well as a 3D model of the sliding surface are estimated followed by the calculation of the soil mass involved by the Kahroud landslide. [ABSTRACT FROM AUTHOR]

This research was mainly aimed at the validation analysis of an integrative approach of the physical-based stability index mapping (SINMAP) with the maximum entropy (MaxEnt) stochastic model for risk analysis of mass movements to identify effectual driving forces. The study area (the geologic zone of the Kopet Dagh-Hezar Masjed) is geographically located in the northeast of Iran, where mass movements had been recorded in the types of slide, fall, compound zone and mudflow. Different layers of information including topography, geology, land use and vegetation, rainfall, and soil properties were extracted and analyzed in the geographic information system. The effective factors on the incidence of each mass movement group were determined based on the results of the Jackknife estimator. The approach of receiver operating characteristic was used to validate the MaxEnt results. According to the validation data set, the area under the curve for the incidence modeling of the slide, fall, compound zone, and mudflow was 0.723, 0.749, 0.729, and 0.727, respectively, which demonstrated good predictions by the SINMAP-MaxEnt hybrid model. The integration of SINMAP with MaxEnt was able to improve the results (up to 3–8%), as compared with employing only MaxEnt, through estimating the hydrological and geotechnical parameters especially in places where we faced a shortage of observational records. Stability index analysis showed that 50% of all recorded points of mass movements were to be found in a naturally stable zone. This examination accompanied with sensitivity analysis demonstrated that significant destabilizing factors, especially anthropogenic driving forces like land-use change have led to slope instability. [ABSTRACT FROM AUTHOR]

Each year, so many devastating landslides occur all around the world causing a lot of life and economical losses. Many of these landslides take place in large populated cities where inappropriate land use and rapid unplanned urbanization highly increase the hazard risk. On July 15, 2020, a landslide occurred on the hillsides near a road construction site in Shiraz city at southwest of Iran. The area of the landslide is estimated to be around 80,000 m2. Based on the reports of local media, 40 homes located adjacent to the toe of the landslide were evacuated. The aim of this study is to carry out a comprehensive study on the aforementioned landslide and to evaluate the factors that had significant effect on occurrence of this landslide. The results of the present study can be used to prevent eventual landslides in the future in Shiraz or at least, to mitigate the dangerous consequences. The sliding materials mainly consist of limestone and highly weathered marl. In the present study, the results of several in situ and laboratory tests on regional rocks are used to analyze the aforementioned landslide in a probabilistic manner. Moreover, a back analysis is carried out to estimate the shear strength of weathered marly clay layers. Identification of the physical and mechanical characteristics of Asmari formation seems crucial, because this formation has various outcrops in the Zagros region. [ABSTRACT FROM AUTHOR]

Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk studies on landslides in the area affected by the Manjil-Rudbar earthquake in Iran in 1990 (M = 7.7). Newmark displacement analysis was used to develop an earthquake-induced landslide hazard map for the blocks representing Chahar-Mahal and Chalkasar near the earthquake epicenter, an area of 309 km2. The input data included soil cohesion, soil friction angle, unit weight of soil, unit weight of water, distance from hypocenter, slope, and earthquake magnitude as effective parameters for landslide occurrence. The hazard map was compared with an inventory map and other research findings. The results indicated that the landslides predicted by ANN covered 50% of the inventory map of the study area (2088 of 4097 slide cells). The results of the current study suggest that the ANN method is relatively efficient for accurate prediction of landslides. [ABSTRACT FROM AUTHOR]

In recent years, increasing efforts have been made to predict the time, location, and magnitude of future landslides. This study explores the potential application of four state-of-the-art data mining models (logistic regression, random forest, support vector machine, and Naïve Bayes tree) for the spatially explicit prediction of landslide susceptibility across a landslide-prone landscape in the Zagros Mountains, Iran. Fifteen conditioning factors and 272 historical landslide events were used to develop a geospatial database for the study area. A two-step factor analysis procedure based on the multicollinearity analysis and the Gain Ratio technique was performed to measure the predictive utility of the factors and to quantify their contribution to landslide occurrences across the study region. Once the models were successfully trained and validated using several performance metrics (i.e., ROC-AUC, sensitivity, specificity, accuracy, RMSE, and Kappa), they were applied to the entire study region to generate distribution maps of landslide susceptibilities. Overall, the random forest model demonstrated the highest training performance (AUC = 0.971; accuracy = 99%; RMSE = 0.120) and ability to predict future landslides (AUC = 0.942; accuracy =87%; RMSE = 0.312), followed by the support vector machine, Naïve Bayes tree, and logistic regression models. The Wilcoxon signed-rank test further proved the superiority of the random forest model for mapping landslide susceptibility in the Zagros region. The insights obtained from this research could be useful for the spatially explicit assessment of landslide-prone landscapes and obtaining a better understanding of the capability of different predictive models. [ABSTRACT FROM AUTHOR]

Landslides are natural hazards that cause severe casualties and financial losses. There are various methods used to analyze landslides; among these, geotechnical and geophysical methods are widely used due to their accuracy and low cost, respectively. In this study, 2D electrical resistivity tomography (ERT) surveys, geotechnical, and field data are used to define the subsurface structure and the geometry of the Nargeschal landslide. To determine the most appropriate array, ERT measurements were performed by Wenner-alpha (Wa), Wenner-Schlumberger (WS), and dipole-dipole (DD) arrays. Furthermore, the relationship between electrical resistivity with change in the degree of saturation and landslide hydrology was investigated by completing multiple surveys at the same location, at different times. Landslide 3D geometry and hydrology were identified by ERT results. Moreover, geotechnical data was used to investigate the Nargeschal landslide and to interpret the ERT pseudo sections. The data from boreholes were used to constrain the depth and consequently the resistivity range that characterize the basal slip surface of the landslide in the ERT pseudo sections. The results of geotechnical investigations indicated that the landslide material had moisture content very close to the plastic limit, with a higher clay fraction and low shear strength at the slip surface. Finally, it must be noted that the simultaneous use of the ERT survey, geotechnical methods, and field investigations led to a complete and accurate characterization of the Nargeschal landslide. [ABSTRACT FROM AUTHOR]

The aim of the presented study is to assess the fractal dimension ( D) and the geometrical characteristics (length and width) of the landslides identified in North of Tehran, Iran. At first, the landslide locations (528 landslides) were identified by interpretation of aerial photographs, satellite images and field surveys, and then to calculate the fractal dimension ( D), we used the computer programming named as FRACEK. In the next step, geometrical characteristics of each landslide such as length ( L) and width ( W) were calculated by ArcGIS software. The landslide polygons were digitized from the mentioned landslide inventory map and rotated based on movement direction. The fractal dimension for all landslides varied between 1.665 and 1.968. Subsequently, the relationship between the length/width ratios and theirs fractal D values for 528 landslides was calculated. The results showed that correlation coefficients ( R), which are different regression models such as exponential, linear, logarithmic, polynomial, and power, between D and L/ W ratio are relatively high, respectively (0.75, 0.75, 0.76, 0.78, and 0.75). It can be concluded that the fractal dimension values and geometry characteristics of landslides would be useful indices for the management of hazardous areas, susceptible slopes, land use planning, and landslide hazard mitigation. [ABSTRACT FROM AUTHOR]

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices. [ABSTRACT FROM AUTHOR]

Here, we investigated the landslide susceptibility in the southern coast of the Caspian Sea, located in the northern part of Iran. This is of high importance for this area as it is highly touristic and agriculturally used and therefore of high value for the local inhabitants. Lack of landslide susceptibility map regarding this area is particularly a main motivation for this study. As a basis for modeling, a field mapping of different landslide types was performed. According to the field studies, this area has a high potential for landslide occurrence, and various kinds of mass movement can be observed all over the area. In order to preparing the area’s landslide susceptibility map, topographic, geotechnical, and landslide data were gathered, digitalized, and stored as raster layers. Then, using the infinite slope analysis, the static safety factor was determined in different conditions of saturation ( m) and thickness ( t) of landslide in order to get the most appropriate landslide susceptibility map. The accuracy of analysis was evaluated by comparing the recent landslides with the FS maps. The results indicated that the map obtained using m = 1 and t = 5 m compared with other maps has more compliance with the area. Therefore, this condition was used as cutoff in preparing the area’s landslide susceptibility map. This map comprises five susceptibility ranges: very low, low, moderate, high, and very high. This map shows that high percent of the region (36 %) is subject to landslides. The landslide susceptibility map was verified using 69 older landslides in the area. The results indicated that most of the landslides in the area were concentrated in high and very high susceptibility classes. The landslide susceptibility map prepared in this study can be practical in engineering projects and architectural activities in the investigated area. [ABSTRACT FROM AUTHOR]

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70 % (55 landslides) for training the models and the remaining 30 % (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7 %) performed better than AHP (81.1 %) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose. [ABSTRACT FROM AUTHOR]