Omid Asadi Nalivan; Majid Rahmani; Farzaneh Vakili tajareh; Asghar Bayat
Abstract
IntroductionIdentification of the areas prone to landslides and the risks arising from them is one of the primary measures in natural resource management and development and construction planning. Considering the loss of lives, financial losses and environmental effects, landslides have been one of the ...
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IntroductionIdentification of the areas prone to landslides and the risks arising from them is one of the primary measures in natural resource management and development and construction planning. Considering the loss of lives, financial losses and environmental effects, landslides have been one of the most important natural disasters in the world and especially in our country, which every year plays an increasing role in the destruction of communication roads, pastures, gardens, residential areas, cause erosion and produces a high volume of sediment in the watersheds of the country. These issues have led to the use of data mining models in geological and geotechnical studies. In recent years, the use of geographic information systems and remote sensing along with machine learning methods has created a new step in landslide occurrence zoning and landslide susceptibility maps with appropriate accuracy. The watershed of Karaj Dam is one of the areas prone to landslides due to mountainous and rainy conditions and many construction due to suitable weather conditions and non-standard road construction. The purpose of this research is to prioritize the factors affecting landslides using the maximum entropy model (MaxEnt model) and to determine areas with landslide susceptibility potential.Materials and methodsThe Karaj Dam watershed is located in the east of Alborz Province. The highest and lowest average annual rainfall is calculated as 1099 and 608 mm, respectively. In this research, in order to determine the areas with landslide susceptibility, among 11 factors affecting the landslide potential of the area, including height, slope, slope direction, distance from waterway, lithology, rainfall, land use, topographic moisture index, surface curvature, distance from the waterway and the distance from the road, the factors were selected and tested for collinearity with the Variance Inflation Factor (VIF) test in SPSS software. From the total of 477 landslides, 70% were randomly classified as test data (334 points) and 30% as validation data (143 points). In this research, the maximum entropy model is used. To determine the most important parameters, the jackknife diagram and the Relative Performance Detection Curve (ROC) were used to determine the predictive power of the model. Landslide points of the studied area were prepared from the database of the General Directorate of Natural Resources and Watershed Management of Alborz Province and field visits.Results and discussionThe results showed that there is no co-linearity between the used factors. According to the Jackknife diagram, rainfall layers, distance from the road, lithology and land use were respectively the most important factors influencing the occurrence of landslides in the study area. The relative performance detection curve showed the accuracy of 90% (excellent) of the maximum entropy method in the training phase and 83% (very good) in the validation phase. According to the final landslide susceptibility map, more than 35% of the study area has high and very high landslide susceptibility potential.ConclusionAccording to the obtained results, it can be said that the MaxEnt model has a high ability to determine landslide-susceptible areas, and due to the high speed and accuracy of the model, it is suggested that it be used in similar research, especially in developing countries. The reason for the lack of facilities and financial resources, as well as the time-consuming nature of identifying landslide sensitive areas, should be used. In addition to natural factors, some human factors such as road construction play an important role in the occurrence of landslides, and in order to reduce the relative risks, it is necessary to avoid changing the ecosystem as a driver of natural disasters. In general, it can be stated that the watershed of Karaj Dam has a high potential for landslide susceptibility, that most of the susceptible areas are located near roads, and because there are many human interventions in these areas. Landslide sensitivity has increased. It is suggested to combine geographic information systems with maximum entropy method in order to determine areas with landslide susceptibility, especially in developing countries like Iran, where access to landslide information and data is limited by time and cost. The results of this research can be used in decision-making and preparation of provincial land as well as urban planning and will play a significant role in preventing and reducing damage caused by landslides.
Mehdi Khalajzadeh; Gorban Vahabzadeh; Sadat Feiznia; Aliakbar Nazarisamani; Seyed Ramzan Mousavi
Abstract
Identification of hydrogeomorphic processes such as normal flood, debris flood and debris flow on alluvial fan (at the outlet of the watershed) is essential due to the type of sedimentary regime and various hazards. In recent years, the lack of field information has led to the need to use models based ...
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Identification of hydrogeomorphic processes such as normal flood, debris flood and debris flow on alluvial fan (at the outlet of the watershed) is essential due to the type of sedimentary regime and various hazards. In recent years, the lack of field information has led to the need to use models based on basic watershed information. The aim of this study is to develop a practical method for predicting the occurrence of various types of flood flow, using physical and geomorphological characteristics of watersheds. In this study, a descriptive-analytical method and some of tools, such as aerial photographs, satellite images, topographic maps were used. First, by descriptive method, field evaluation of sediments of various types of flood currents was carried out on July 19, 2015 in 70 sub-watersheds of in Karaj Dam Watershed. Results showed that out of 70 sub-basins, 30 sub-basins, debris flow, 16 sub-basin debris flood and 24 sub-basins were normal floods. Then, in the analytical method, 32 geomorphometric features of watersheds by 25-meter spatial digital model (DEM) and five geomorphological features of sub-watersheds by mass movement maps were extracted and then were transferred to the SPSS statistical program to determine the relationship with the type of flood flow. The results of ANOVA and Bonferroni multiple comparisons showed that four morphometric factors “main channel length, basin perimeter, mean basin width and basin length” were identified in differentiation of flood flow types. To achieve the two appropriate key parameters and threshold values, four variable pairwise were copaird by pair in six triple scatter plots. The results showed that “The main channel length (Lm) and the mean basin width (Wb)” has the least total errors of the observation streams, were selected as the most appropriate factors for predicting flood flows. In long basins with Lm>4 km, normal flooding occurs, and in short basins with Lm1 km, floods occur, and if Wb
Ramin Salmasi; Hamid Reza Peyrowan
Abstract
The most of the soil erosion and sediment yield in the Talkheh Rood watershed are mainly related to marl formations. This study was conducted to evaluate physico- chemical properties of Talkheh Rood marl samples in rill, gully, mass and badland erosion types. For this purpose, marl samples were taken ...
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The most of the soil erosion and sediment yield in the Talkheh Rood watershed are mainly related to marl formations. This study was conducted to evaluate physico- chemical properties of Talkheh Rood marl samples in rill, gully, mass and badland erosion types. For this purpose, marl samples were taken from marl sediment of homogenous work units. Twenty four samples were selected, in total. Some of main physic - chemical properties of these marls were measured in lab. These properties were pH, EC, CEC, Lime, gypsum and OC content, SAR, Na, K, Cl, Ca, Mg, clay, silt and sand percentage. These data were interpreted by ANOVA and mean comparisons analysis methods. Results showed that pH, gypsum content and sand percentage had statistically significant differences between four erosion types. Mean comparison showed that significant difference of pH, was between mass and badland erosions, sand between badland and gully ones, and gypsum, rill and gully, also badland and gully ones.
Jamal mosaffaie; Majid Ownegh
Abstract
Landslide is one of the natural hazards that makes numerous financial and life damages each year. By landslide hazard zonation, we able to detect susceptible areas to landslide and with applying developed methods and suitable management the abundance of land sliding and the amount of damages will be ...
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Landslide is one of the natural hazards that makes numerous financial and life damages each year. By landslide hazard zonation, we able to detect susceptible areas to landslide and with applying developed methods and suitable management the abundance of land sliding and the amount of damages will be reduced. In this study potential landslide hazard evaluated using multivariate regression model at a part of Alamout watershed in general level (1:50000 scale). So first, landslide distribution map of area prepared using study of air photos and field surveying. After reviewing available resources along with reviewing the benefit of experts, all factors that can affect landslide were extracted and among them eight parameters including (lithology, slope percent, aspect, height, distance to fault, land use, rainfall and earthquake acceleration) were selected as landslide effective factors. AHP and pair comparing technique were used for numerical weighting to qualitative categories of land use, aspect, and lithology parameters. Homogeneous units map prepared using overlaying 8 maps of landslide key factors, and by crossing of homogeneous map and landslide distribution map. Categories of each parameter were detected in each landslide, and with weighting average of them, the effect of each parameter was determined in each landslide. Therefore 84 observations prepared for statistical analysis of landslides. Results showed that 5 parameters including lithology, slope, height, distance to fault and land use have meaningful relation with landslides that determining coefficient between these parameters as independent variables and logarithm of landslides area as dependent variable was 60.7%. Landslide hazard zonation map and landslide distribution map were crossed and efficiency of model was evaluated. The Chi square test was used for comparing of difference between hazard classes of model. Results show that model has higher efficiency in higher classes of hazard. Results show also measured chi square rate is meaningful at 99% of confidence interval, and there is suitable separation among landslide hazard classes.
