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:: Volume 5, Issue 4 (6-2017) ::
ژئو کمی 2017, 5(4): 121-136 Back to browse issues page
Landslide hazard zonation by evaluating environmental variables using network analysis (case study: Bijar city)
Abstract:   (1160 Views)
Predicting landslides in a region, in addition to the reduction of risks and losses caused, can be useful in analyzing and forecasting the development of a region. Many factors are involved in the occurrence of these natural phenomenon. In addition to natural factors such as lithology, tectonics and climate, human role is also significant.Identifying these factors and landslide hazard zonation can help in better evaluating of area potential talents. And it divides the area in terms of risk to the several sub-districts to make planning easier. Iran is largely due to the mountainous topography, the tectonic and seismic activity has high natural potential for extensive landslides.Kurdistan province is the third largest province in terms of landslide after Mazandaran and Golestan. If the ranking criterion is the area, the province is ranked the highest.Bijar city in the Kurdistan province, with a combination of factors mostly mountainous topography, climatic and lithological conditions and locating between two fault of Tabriz in East and Zarineh River in the West which a large number of subdirectories are between these two faults, has great potential for the occurrence of extensive range of landslides.In recent years, extensive researches have been carried out by scientists including geomorphologs, with the aim of identifying risk factors for landslide hazard zonation and also to choose the best model for optimal management of the area.The study, amid at the investigating the slide system in the Bijar area and the importance of effective factors, has applied ANP method as a new model in the landslide.
General method of this research is a combination of theoretical studies, fieldwork along with the application of model and software statistical analysis.To achieve the goal, first, the effective layers of slope, aspect, elevation, distance from fault, distance from roads, distance from the river, floodway density, lithology and land use in GIS were prepared. And then weighting to the sub-criteria within the group and the criteria between groups were performed.In the next step, available weights were put into Super Decision. Matrix and final weighting, according to input data, were done by the software to reduce the high rate of error.Finally, by pooling layers, landslide hazard zonation map was prepared on the basis of the range model with high risk, relatively high risk, medium risk, relatively low risk and low risk. In the last step, the assessment and verification of ANP model was done with real data to determine how well the model for the area is.
To achieve the goal, binary comparison of each of the main criteria and sub-criteria was performed based on a quantitative scale 9 hours.In comprising the criteria, one of the criterion was controlled to gain its importance and its special vector. After comparing the criteria and doing the matrix, their adjustment coefficient was controlled which was not over 0/1, considered by the Thomas hour.After the comparison, their special vector was put into Super Decision in the form of raw data and were used as the imbalanced matrix.Then, imbalanced super matrix became balanced by multiplying into cluster matrix. In the next step, to show and provide significant weight of parameters, the balanced matrix was limited.In the final step, the vector of ultimate importance was normalized and contribution of each of the variables was presented in landslides. These numbers were obtained as a decimal and between one and zero which, in fact, are as the same criteria in effect.The results showed that the distance from the fault has the greatest impact and land use has less impact on the occurrence of landslide and landslide zonation. Finally, in Arc GIS using model data of ANP, the final map was drawn which is the same zonation map.Reviewing the criteria affecting landslide of the area, using ANP, showed that the impact of the fault and height above sea level is more than the other criteria on the process.In the final stage of the study, to evaluate the effectiveness of the model as well as the accuracy using GIS, the landslide hazard zonation map was prepared through integrating and comparing the amount of overlap. The results showed that 94 landslides are at the relatively high and relatively high risk range which shows the accuracy of the study.
Zoning and mapping out of landslide within the Bijar city shows that Bijar city and its dependencies are relatively disposed and susceptible to landslides.Systematic results, based on zoning and validation, showed that 41 percent of the study area is at relatively high and high risk that, in the planning process, should be taken into consideration through the assessment of variables.Validation results showed that the ANP, according to network analysis model is successful and can be used in other regions of Iran.
Keywords: Zonation, Landslide, GIS, network analysis, Bijar
Full-Text [PDF 602 kb]   (330 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/06/19 | Accepted: 2017/06/19 | Published: 2017/06/19
1. Anbalgan, R., 1992, Landslide hazard evaluation mapping in mountain terrain, Engineering geology, Vol.32, pp. 269-277.
2. Berberian, M., King G. C. P., 1981, Towards a paleogeography and tectonic evolution of Iran, Canadian Journal of Earth Sciences, Vol. 18, No. 2, pp. 210-265.
3. Bulut, F., Boynukalin, S., Tarhan, F., Ataoglu, E., 2000, Reliability of Landslide Isopleth Maps, Bulletin of Engineering Geology and the Environment, Vol. 58, Issue. 2, pp. 95-98.
4. Chen, W., He, B., Zhang, L., Nover, D., 2016, Developing an integrated 2D and 3D WebGIS based platform for effective landslide hazard management , International Journal of Disaster Risk Reduction, Vol. 20, pp. 28-38.
5. Dai, F. C., Lee, C. F., Xu Z. W., 2001, Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong, Environmental Geology,Vol. 40, Issue. 3, 381-391.
6. Wang, Y., Bouten, W., Chen, Q., 2015, using statistical Learning Algorithms in Regional Landslide Susceptibility Zonation with Limited Landslide Field Data, Journal of Mountain Science, Vol. 12, Issue. 2, pp. 268-288.
7. Komac, M., 2006, A landslide susceptibility model using the analytical hierechy process method and multivariate statistics in per alpine Slovenia, Geomorphology, Vol. 74, 1, pp.17-28.
8. Melchiorre, C., Tryggvason, A., 2015, Application of a fast and efficient algorithm to assess landslide-prone areas in sensitive clays in Sweden, Natural Hazards and Earth System Sciences, Vol. 15, 12, pp. 2703-2713.
9. Mora, S., Vahrson, W., 1994, Macrozonation methodology for landslide hazard determination, Bulletin of the Association of Engineering Geologists, Vol. 31, 1, pp.49-58.
10. Ramesh, V., Anbazhagan, S., 2015, Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models, Environmental Earth Sciences, Vol. 73, 12, pp. 8009-8021.
11. Yalcin, A., 2008, GIS based landslide susceptibility maping using analytical hierarchy process and bivariate statistics in Ardesen(Turkey), Comparisons of result and confirmation catena, Vol. l72, 1, pp. 1-12.
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Landslide hazard zonation by evaluating environmental variables using network analysis (case study: Bijar city). ژئو کمی. 2017; 5 (4) :121-136
URL: http://geomorphologyjournal.ir/article-1-763-en.html

Volume 5, Issue 4 (6-2017) Back to browse issues page
مجله پژوهش‌های ژئومورفولوژی کمّی Quantitative Geomorphological Researches
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