Geomorphology
Fariba Esfandyari Darabad; Ghobad Rostami; Raoof Mostafazadeh; Mousa Abedini
Abstract
In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including ...
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In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.
shahram roostaei; davood mokhtari; christin jananeh
Abstract
1-IntroductionMass movements of the earth's surficial materials downward the slopes is called slope instability, which is affected by the earth gravity, while the rate of material mobility increases by the presence of water in the sediments. Each year, slope instabilities cause enormous economic damages ...
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1-IntroductionMass movements of the earth's surficial materials downward the slopes is called slope instability, which is affected by the earth gravity, while the rate of material mobility increases by the presence of water in the sediments. Each year, slope instabilities cause enormous economic damages to roads, railways, power transmission and communication lines, irrigation and watering canals, ore extraction, as well as oil and gas refining installations, infrastructures in cities, factories and industrial centers, dams, artificial and natural lakes, forests, pastures and natural resources, farms, residential areas and villages or threaten them. Nowadays, many instabilities are resulted by human intervention and manipulations. One of the human factors effective in the instability occurrence is the construction of roads. Road construction, especially in mountainous areas, increases the probability of occurrence of various types of instabilities, as it changes the natural balance of the slopes and causes deformations in the land. Each year, lots of casualties and financial losses are imposed by the occurrence of various types of instabilities in the slopes overlooking the roads, which also cause the destruction of many natural resources in the country. However, the construction of roads, highways and freeways is necessary and unavoidable in today’s life.The Karaj-Chaloos road and the Tehran-North highway are two routes that connect Tehran as Iran’s capital, with the southern shores of the Caspian Sea, although suffering frequent slope instabilities.2-Methodology This contribution aimed to study slope instabilities along these roads using logistic regression method. In this regard, layers of 14 effective factors were identified, comprised of elevation classes, slope, aspect, geology, land use, precipitation, distance from fault, river and road, normalized difference vegetation index (NDVI), climate, slope length (LS), stream power index (SPI) and topographic wetness index (TWI). Consequently, maps of the factors responsible for instabilities were prepared as separate layers in the GIS environment and transferred into the Idrisi software. The whole procedure included: (1) preparation of digital elevation model (DEM), river and fault layers based on the 1:25,000 topographic map of the area, as well as distance maps from rivers and faults, (2) creating slope and aspect maps from DEM, (3) preparation of land use and NDVI maps of the region based on unmatched classification of Landsat 8 image of OLI sensor, (4) preparation of geological map, (5) preparation of precipitation and climate layers based on the information obtained from the meteorological organization, (6) creating LS, SPI and TWI layers based on the DEM, (7) conversion of the distribution data of the regional instabilities using Landsat satellite and Google Earth images, (8) correlating the information layers with the regional instability map and calculating their density per unit area, and (9) performing the logistic regression model using Idrisi software.3-Results and Discussion Results obtained by applying logistic regression model showed that the most important factors affecting slope instabilities in the Karaj-Gachsar road area were the distance from river, climate and SPI, while those for the Tehran-Soleghan road area were the distance from fault and road and climate. 34.95 percent of the lands in the Karaj road area had medium to high potential for instability occurrence; 54.87 percent of the occurred instabilities corresponded to these areas. Moreover, 4.97% of the Karaj road area had a very high potential for instabilities, which correlated with almost 9% of the occurred instabilities. This was while 27.14% of the Soleghan road area possessed medium to high potential for instabilities, within which 86.26% of the instabilities have occurred. Furthermore, 4.57% of the Soleghan road area showed very high risk in terms of instability occurrence, encompassing 61% of the occurred instabilities. According to the prepared maps, the southern and middle parts of the Karaj-Gachsar road, as well as another part in the northwest of the study area had the highest potential for the occurrence of instabilities, whereas in the Tehran-Soleghan road area, the middle and southern parts and a small section in the north of the area had the highest potential for instability occurrence. By comparing these two areas, it was conceived that areas with medium to high potential of instability in the Soleghan road area were less than those of the Karaj road area (27.24% and 34.95%, respectively). However, the percentage of instabilities occurred in the Soleghan road area was much higher (86.26%) than the Karaj road area (54.87%). The high value of the ROC index and its proximity to the end value of 1 in both areas indicated that instabilities strongly correlated with the probability values derived from the logistic regression model. Additionally, the assessment of the instability potential map by the SCAI index showed that there was a high correlation between the prepared risk maps and the occurred instabilities, which have been confirmed by field surveys. The obtained results were in a good agreement with the general opinion that SCAI decreases especially in high and very high risk classes indicating a high correlation between the prepared risk maps and the occurred instabilities and field surveys in both areas.4-ConclusionThe results of this investigation showed that the logistic regression model was suitable for preparing the zonation of the probability of instability occurrence along the edges of the studied roads. Moreover, in addition to natural factors, the human-made factors and particularly unsystematic road construction can play an important role in the instability occurrences on the slopes overlooking the roads. In order to reduce the relative risks and increase the stability of the slopes, it is necessary to avoid manipulating the ecosystem and changing the current land use as much as possible, in addition to policy making for constructions in accordance with geomorphological and geological features of the area.Keywords:Instability, Logistic Regression, Tehran-North highway, Karaj-Chaloos road, Risk zonation.
Sayyad Asghari; Rasool Hasan zadeh; Soheil Raoofi
Abstract
1-Introduction Instability of natural slopes is one of the geological and morphological phenomena that has a significant role in changing the form of surface of the earth, and when it affects human activities, it can become a dangerous phenomenon (Esfandiari, 2006: 113). Landslides as geological events ...
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1-Introduction Instability of natural slopes is one of the geological and morphological phenomena that has a significant role in changing the form of surface of the earth, and when it affects human activities, it can become a dangerous phenomenon (Esfandiari, 2006: 113). Landslides as geological events related to the transportation of soil / heavy rock materials and assessment of its sensitivity, is an important task for local authorities to plan and reduce the land (Xialong Deng, 2017: 2). Therefore, many attempts have been made to assess the dangers of mass movements, and it is suggested to have its reduction methods based on the key characteristics of the slip, including scope and extent, volume, startup mechanism and recurrence, and subsequently, make decisions (Kuo Jeong Chank et al., 2018: 700). (Hemati and Hejazi 2017: 24-7) evaluated the landslide hazard zonation of Lavasanat watershed using logistic regression statistical methods and the result was stated in this way that in the studied area, areas with high risk of zoning, had a large share of the area amount of the region. Aliabad basin with the southwest - northeast trend in the geographical coordinates of - located in the east and - located in the north latitudes of the northeast of East Azarbaijan Province and southeastern part of Horand County.(Figure1) Figure (1): Geographic location of Aliabad watershed 2-Methodology 1- Topographic map (1: 50000) and geological map of Kaleybar region (1: 100000). 2- Landsat satellite images of 8 OLI sensors 3- GPS devices 4- Maps of the faults, slopes, isohyet, isotherm, evaporation, land use, elevation and hydrology 5- Envi 5.3 software 6- Statistical software of SPSS, version 16. For zoning the risk of rock falls, nine layers of information including slope, hypsometry of the region, isohyet, isotherm, evaporation, distance from the fault, distance from the river, land use and lithology were used as independent variables and to prepare the layers in Arc GIS, 1,500,000 topographies and 1.100000 geology maps were utilized, and Landsat 8 satellite imageries were used with the OLI sensor to produce the land use layer. So, after preparing the considered data, the layers were classified as raster, and in their descriptive table, a column called the standard weight was added and the classes related to each layer were calculated using a sum ranking method. In this research, the rock fall layer was considered as the dependent variable and the 9 presented layers were considered as independent variables and all layers had been evaluated in the normalization of the weight between zero and one per pixel; based on the proportion table method, each layer, having 500 weighted pixels that overall included 5000 pixels, was entered into the SPSS environment and regression analysis was performed thereof. Independent variables, including 9 variables, consisting of three PhDs in geomorphology and two Phd in geology were selected based on exports opinions considering their importance in creating and strengthening the dependent variable were weighted between zero and one numbers. 3-Results and Discussion The Chi square test for each of the independent variables, separately, showed that there was a significant relationship between the independent variables and the dependent variable, and the effects of these variables on the dependent variable was acceptable. The numerical value of R was 0.953, and if the R value was closer to one, it would indicate the high validity of the test. The numerical value of the coefficient of determination of the independent variables relative to the dependent variable was 0.909, which indicated the high validity of the significance of the test, because it was closer to number one. Of course, it is clear that the value of the determination coefficient in Pseudo R Square was determined to be good, so the adjusted coefficient of determination was considered whose numerical value was 0.907. These findings indicated that roughly 90 percent of rock falls occurred in the Aliabad basin have been affected by these 9 estimated independent variables. Given that the statistical analyzes confirmed the validity of the effects of independent variables on the dependent variable according to the weightings of the experts in terms of zero and one for each variable as well as the importance of the variables in relation to each other as a binary comparison, the zoning of the risk of rock fall for the Aliabad watershed of the Horand basin was done using Arc Gis software, and in this zonation, five falling risk classes were used including very high, high, medium, low and very low . 4- Conclusion lithology and the distance from the fault and river and foot slopes were the most important factors in the formation of rock falls since the drainage system of the basin exactly followed the fault zone. The reason for this issue can be analyzed in the way that the longitudinal distance of the highest parts of this region, from the basin to the Aliabad River was lower, which has caused the slope of the basin to perform deep slices to achieve a balance in the slopes and hydrology. The southern parts of the basin are considered as one of the most susceptible basins in the geomorphologic phenomenon of rock falls and destructive cones due to the existence of alluvial formations and the lack of proper slopes and the relative reduction of the fault to the northern and eastern parts despite having significant heights and very low and low status of zonation in the risk of rock falls, and in the southwestern part of the basin, a presence of rocky outcrops in the presence of permeable cones has been also observed. This issue should be addressed to the authorities in order to avoid serious damages to the lives of the inhabitants of the basin, so that the potential risks of this phenomenon could be controlled as much as possible including: threatening communication routes and threatening rural villages and damaging electrical and telecommunication facilities, therefore, infrastructure solutions should be applied in this regard.
Keyvan Mohammadzadeh; Seiran Bahmani; Mohammad Hossein Fathi
Volume 4, Issue 11 , September 2017, , Pages 127-148
Abstract
Introduction
Iranian territory has the main prerequisites for the occurrence of a wide range of landslides due to its mountainous topography, tectonic activities, high seismicity, and different geological and climatic conditions. Therefore, reducing the effects of natural disasters, particularly landslides, ...
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Introduction
Iranian territory has the main prerequisites for the occurrence of a wide range of landslides due to its mountainous topography, tectonic activities, high seismicity, and different geological and climatic conditions. Therefore, reducing the effects of natural disasters, particularly landslides, is one of the key challenges for land-use planners and policymakers in this field. In this study, the southern side of the Ahar Chai basin from Nasirabad Village to Sattarkhan Dam is evaluated for the probability of the landslide occurrence. This region is highly susceptible to landslide occurrence because of the extensive manipulation and its natural conditions. Indeed, the occurrence of the large shallow landslides in this region is an indication of this susceptibility. In this study, Linear Regression Model has been used to prepare the landslide zonation.
Methodology
The study area was the southern sides of the Ahar Chai River, from Nasirabad village in Varzaghan to the Sattarkhan Dam, with an area of 128 km2. In order to study the potential of the landslide occurrence in this region, nine main factors including slope, slope direction, lithology, land use, precipitation, distance from the fault, distance from the river, distance from the road, and vegetation were identified. The model which was used in this study was Logistic Regression. This model is one of the predictive statistical methods for dependent variables in which zero and one respectively indicate the occurrence and non-occurrence of landslides. In addition, instead of being linear, the regression of the variables is S-shaped or logistic curve and the estimations are in the range of zero-one. Indeed, values close to zero indicate the low probability of the occurrence and values close to one indicate the high probability of the occurrence.
Discussion
In Logistic Regression model, after entering the data into the Logistic Regression model and using the effective parameters in Idrisi software, the coefficients of the model were extracted. A value of 965, which represents a very high correlation between the independent and dependent variables, was obtained for the ROC index. After determining the validity of the Logistic Regression model, using the above indicators, landslide sensitivity zonation map was prepared. In the present model, the land use factor with the highest coefficient was the best predictive variable in determining the probability of the landslide occurrence in this region. In addition, the SPI index and the distance from the fault had respectively the second and third highest coefficients. After zoning the landslide, the slip area was calculated for each class and its results showed that zones with highest risk had the lowest area percentage and these areas were located in the western slopes.
Conclusion
The results showed that while land use, lithology factors, and SPI index with positive coefficients had higher correlation, the other factors with negative coefficients had lower correlation. Based on the map, the western, southern, and the north-eastern parts of the region have the highest potential for landslide occurrence. Furthermore, the high value of the ROC index and its proximity to number one indicates that landslides in the study area have a strong correlation with the probability values derived from the Logistic Regression Model. In addition, the assessment of the SCAI scaling hazard zonation map shows that there is a high correlation between the hazard map with the existing slip points and the field observations of the area. It can be said that, in addition to the natural factors, some human factors including unstructured road construction may play an important role in the occurrence of the landslides. It is also necessary to avoid making changes in the ecosystems and land use. Finally, any policies to construct structures should be commensurate with the geomorphologic and geological conditions.
