An analysis of landside risk in the Arasbaran seismic zone using ANP and LR models (Case study of Ghoshdagh-Arasbaran fault zone in the East Azerbaijan)
Extended Abstract Introduction Earthquakes and landslides are the most harmful natural disasters that have synchronization and time and space correlation with each other. This study was carried out in seismic zone affected by Arasbaran earthquake (Qaradagh), in which there were a lot of casualties and destruction. On 11 August 2012, the Varzeghan twin Earthquakes [Mw 6.4 and 6.2] struck Varzeghan, Ahar and Heris region in NW of Iran. It killed more than 306 people and a large number of people were injured and has caused increment in slope instability, and transformation of geomorphic processes into risk factors. Methodology In this research, landslide susceptibility was zonated by using two models of analytic network process (ANP) and logistic regression in Qoshadagh fault system and an appropriate model was introduced. Our analysis of geohazards distribution allowed evaluation of geomorphic, climate- hydrologic, human-land use and morphologic controls on earthquake-induced-land sliding, process mechanisms, and hazard process chains, particularly where they affected local populations. For this study, both the Sentinel-2A MSI and Landsat-8 OLI data (2017) were used. Fourteen effective factors of landslide (Lithology, Land use, Fault, DEM, Climate, Aspect, TWI, SPI, Slope, Road, River, Rain, LS & Soil) were generated in ArcGIS, and then weight of each factor was determined in Super Decisions software, and final zonation maps were regained from ArcGIS afterward. Results and discussion After preparing the standard maps and options, the network analysis method (ANP) and logistic regression were used to investigate the susceptibility of land slides. In order to implement the network analysis process, the related criteria layers were developed in ArcGIS software. Then, in the SuperDisign software environment, the main model of the network analysis process was designed based on completed questionnaires by the experts. Then, a logistic regression model was used to analyze the spatial relationship between the land slide event and the effective factors in this event. The maps related to the factors affecting the land slides of the study area, which are independent variables in the land slide event, were introduced into Idrisi-Selva software and processed for logistic regression modeling. Landslide distribution layers in the area were also converted to the binary map 0 and 1 by the Calculator Image function. This means that the slip pixels on the map are shown with the number 1 (slipping) of non-slip pixels with 0 (no slip). Finally, after entering the data into the logistic regression model, the coefficients of the model are extracted using the effective parameters in the Idrisi-Selva software. Distance from the fault and precipitation factors had the most, and Land Use Factor had the least effect on landslide occurrences. Occurrence of 62.2% and 71.1% of landslides in high and very high risk classes in ANP and logistic regression, respectively, indicated an acceptable accuracy for landslide prediction maps. Validation results of methods with ROC index showed that AUC of the maps in model was 85.52%, and in analytic network model it was 81.35%, with a standard error of 0.06; while both represented a very good predictive capability Conclusion The purpose of the present study was to study the ability of this model in comparison to other methods, in addition to modeling the sensitivity of Earthquake-Induced-Landslides using the network analysis method (ANP). Therefore, effective factors in the field of scaling were identified and in the Super Decision software environment, the weight of each factor was determined. Then, the weights received in ArcGIS software environment were converted to the final map of the zoning of the landslide. Accordingly, among the fourteen effective factors in the occurrence of landslides in the area, the distance from the fault has the most effective factor in the two models (ANP) and (LR) and has the highest coefficient of effect on land-slip occurrence while land use in Both models have the lowest coefficient. As expected, the main faults, in particular the fault and the fault lines, and interspersed with the twin Arasbaran earthquakes, as well as the northern and northeastern slopes, are more susceptible to instability. The map of the model implementation categorized the susceptibility of earth sculpting in 5 classes with very low, moderate, high and very sensitive sensitivity. Very high and high risk classes have been shown to be 6.6% and 13.4% of the area of the region, prone to hazardous landslides. These results show a high correlation and correlation with the model implemented in the logistic regression method, which is 5.8% and 17.7%, respectively. The occurrence of 62.2% and 71.1% of landslides in high and very high risk classes, respectively, in ANP and logistic regression, indicate the acceptable accuracy of predicted maps for landslide. The results indicate that ANP methods and logistic regression are accurate in the study of landslide in the area affected by the Arasbaran earthquake. Logistic Regression model had better results. Using these methods together and comparing them with regard to the dependencies of landslip issues can be very useful for identifying areas prone to landslide. As suggested, these methods have acceptable results in analyzing the sensitivity to landslides. The highest density of landslides, even old landslides, are not accidental in the two-axis seismic centers with magnitudes of 6.4 and 6.2 in 2012, indicating a history of seismicity and high tectonic activity in the area. Keywords: Analytical Hierarchy Process, Logistic Regression, Landslide hazard zonation, Arasbaran earthquake, Qoshadagh Fault Zone.
quantitative geomorphological researches, Volume:8 Issue:1, 2019
70 - 88
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