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In this research, landslide susceptibility map of the Fahliyan sub-basin was provided employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony optimization (ACOR) and differential evolution (DE) algorithms. Forty-three out of 61 landslides (70%) were employed to provide landslide susceptibility map and 18 landslides (30%) to validate the models. Thirteen landslide controlling factors including altitude, plan curvature, slope angle, aspect, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, TWI, SPI, land use, and lithology were employed to provide the map of landslide susceptibility. Weights of every effective factor class and effective factors were calculated based on frequency ratio of landslides relative to the class area and entropy model. The landslide susceptibility maps were generated by the GIS-based algorithms, and the resultant was validated using the training (70%) and test (30%) data of landslide locations for success and prediction rates, respectively. According to the entropy model, distance to road, rainfall, and SPI are the most effective factors on landslide occurrence in the area. The area under the curve (AUC) of ROC for the ANFIS, ANFIS-ACOR, and ANFIS-DE algorithms ranges from 0.845 to 0.946 for success rate curves and 0.793 to 0.924 for prediction rate curves, respectively. Therefore, performances of the analyzed models of landslide susceptibility are good to excellent. The success rate curves suggest that the employed algorithms have high prediction performance, but the success rate curves indicate that the ANFIS-DE algorithm has the best estimation performance (0.946) with respect to the other models.

Applications of Synthetic Aperture Sensors (SAR) and particularly Differential Interferometric Synthetic Aperture Radar (DInSAR) have provided new opportunities for detecting and monitoring of slow and even fast land deformations such as landslides and also updating their inventory maps. Employing this technique has made possible continuous detection and monitoring of small land movements with high precision over a wide spread area. In this study, two image series including 12 radar images with descending orbit acquired by ASAR sensor of ENVISAT satellite and 10 radar images with ascending orbit collected by PALAR sensor of ALOS satellite were selected and processed by the DInSAR method in order to detect landslides in the Doab-Samsami basin in Chaharmahal and Bakhtiari province, Iran. Landslides detected in the study area cover over 5959 hec according to the processing of ASAR and PALSAR images collected between 2003 and 2011, whereas landslides detected by field studies cover over 5056 hec. Based on the results of the radar processing technique for detecting and mapping of landslides, the ASAR images can provide more details of slides due to their shorter wavelengths but the PALSAR images have comparatively greater penetration and lower incoherence due to the longer wavelengths. Results of the receiver operator characteristic (ROC) method show a well agreement between the landslides map provided by the DInSAR approach and field study. The area under the curve of receiver operator characteristic (ROC) curve was estimated to be 0.95 with a standard deviation of 0.02 at 95% confidence level. The Cohen’s Kappa of 0.61 indicate relatively good conformity between classification of the detected landslide distribution in the study area based on the DInSAR method and field survey.

The metropolis of Isfahan that is an important historic and tourist city has faced subsidence in recent decades. The purpose of this study is to evaluate the potential and pattern of progressive subsidence in Isfahan Metropolis, located in the middle part of Iran. For this purpose, geotechnical and hydrogeological studies are conducted. Many samples from the 266 boreholes throughout the study area are studied. Many different tests including grading test, soil type, soil thickness, Atterberg limits, plasticity index, void ratio, and consolidation parameters are done. Also, the fluctuations of groundwater table from 2000 till 2014 were also investigated. Then, the results of the tests were combined with the spatial analysis in the GIS software to determine the critical locations as well as the advancing pattern of progressive subsidence in Isfahan Metropolis. The results revealed that moving from the South-East toward the North-West in the study area, the decline in groundwater head was increased from 4 to 36 m. Based on the results, the most severe subsidence occurred in the western border of the city and was calculated to be about 73 cm over the 14 years period. Additionally, inelastic skeletal specific-storage (Sskv) was calculated for fine-grained sediments in the mentioned time span. According to the interpolation map, the maximum values of the inelastic skeletal specific-storage, as well as the most expected subsidence due to a gradual decline in groundwater head, was occurred in the central part of the city.

One of the important issues in identifying susceptible landslide locations is to select effective factors in the landslide of studied areas. The complexity of this issue is due to the fact that each of the factors is a part of the desired criteria, and the selection among them is a multi-criteria decision-making problem, requiring a structured and systematic approach. In the study area, the presence of large outcrops, as well as relatively thick soils formed on the formations, has caused a significant level of the province to potentially have the conditions for the movement of the tides. In addition, the situation is exacerbated by inappropriate use of natural resources, and construction of numerous rural and forest roads. In this paper, multi-criteria decision-making (MCDM) such as the fuzzy analytical hierarchy process (FAHP) and Dempster–Shafer (DeS) method was used to detect susceptible landslide locations, and output maps were verified applying the prediction–area (P–A) method for landslide purposes. The study has utilized P–A plot to evaluate and compare the obtained results of FAHP and DeS methods. The results indicated that the normalized density value achieved from P–A plots was significantly higher for the FAHP than the corresponding value for the DeS. In other words, FAHP, compared to DeS, resulted in the successful detection of landslide occurrences and more accurate classification of the study area.

Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides.

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.