Landslide Modeling with Strategy Management in Urban Planning (The Case Study: Tabriz City)
Subject Areas :
Sustainable Development
ziba Beheshti
1
,
Alirezeza Gharaguzlu
2
,
Seyed Masoud Monavari
3
,
MirMasoud Kheirkhah Zarkesh
4
1 - Department of Environmental Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Faculty of civil water and environmental engineering Shahid Beheshti University, Tehran, Iran. *(Corresponding Author)
3 - Department of Environmental Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
4 - Department of Environmental Science, Soil Conservation and Watershed Management Research Institute, Tehran, Iran.
Received: 2020-01-28
Accepted : 2020-07-11
Published : 2021-08-23
Keywords:
landslide sensitivity,
Environmental Impact Assessment,
strategy management,
Mitigation,
Abstract :
Background and Objective: Due to increasing constructions on clay and marl hells in part of Tabriz and because of the unfavorable quality and characteristics of the soil and its liquefaction during earthquake, the area is also exposed to the risk of landside. This paper attempts to demonstrate the risk of landslide in Tabriz using visual and statistical evidence.
Material and Methodogy: landside susceptibility assessment was performed by means of Evidential Belief Function Model. Then The environmental impacts assessment of landslide were performed using promethean II model in three environmental, economic, and social phases, and at end, the landslide strategy plan was developed to help decision makers, the statistics used in this research are related to year 2016.
Findings: 82/9% of Tabriz areas are at risk of landslides. High construction densities were identified with residential areas below 75 m2. Access to the city’s road network is less than 30%. 142 hectares in health centers, 853hec in facilities and equipment, 430hecin urban green space deficiency was identified. More than 70% of vital centers require strengthening.
Discussion and conclusion: required zones in medical department, security, urban green space, vital centers require strengthening, facilities and equipment presented. The average landslide velocity from 1956 to 2020 is 41/65 meter. High slope and location difference, the texture of the earth and its non-dense layers, northern slope snow catcher, fine texture, the gradual erosion of sediments is effective in creating landslides over time.
References:
Erener, A., & Düzgün, H. S. B. (2010). Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of more and Romsdal (Norway). Landslides, 7(1), 55-68. doi:10.1007/s10346-009-0188-x
Biswajeet, P; Abokharima, MH; Neamah jebur, M. (2014). Land subsidence susceptibility mapping at Kinta valley using the evidential belief function model in GIS. Natural hazards, volume 73, issue 2, pp. 1042-1019.
Dietrich, W. E., Reiss, R., Hsu, M., & Montgomery, D. (1995). A process-based model for colluvial soil depth and shallow land sliding using digital elevation data. Hydrological Processes, 9(3), 383-400. doi:doi.org/10.1002/hyp.3360090311
DFDA/CRED. (2010). Annual disaster statistical review 2009. from Center for research on epidemiology of disasters http://www.cred.be/sites/ default/files/ ADSR_2009
Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1), 15-31. doi:https://doi.org/10.1016/j.geomorph.2004.06.010
DFDA/CRED. (2010). Annual disaster statistical review 2009. from Center for research on epidemiology of disasters http://www.cred.be/sites/ default/files/ ADSR_2009
Nadim, F., & Kjekstad, O. (2009). Assessment of global high-risk landslide disaster hotspots. Landslide- disaster risk reduction, 213-221.
Petley, D. (2012). Global patterns of loss of life from landslides. Geology, pubs. Geoscience world. Org, 40(10), 927-930.
Berberian, M. (1976). An explanatory note on the first seism tectonic map of Iran, a seism tectonic review of the country. Retrieved from.
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Erener, A., & Düzgün, H. S. B. (2010). Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of more and Romsdal (Norway). Landslides, 7(1), 55-68. doi:10.1007/s10346-009-0188-x
Biswajeet, P; Abokharima, MH; Neamah jebur, M. (2014). Land subsidence susceptibility mapping at Kinta valley using the evidential belief function model in GIS. Natural hazards, volume 73, issue 2, pp. 1042-1019.
Dietrich, W. E., Reiss, R., Hsu, M., & Montgomery, D. (1995). A process-based model for colluvial soil depth and shallow land sliding using digital elevation data. Hydrological Processes, 9(3), 383-400. doi:doi.org/10.1002/hyp.3360090311
DFDA/CRED. (2010). Annual disaster statistical review 2009. from Center for research on epidemiology of disasters http://www.cred.be/sites/ default/files/ ADSR_2009
Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1), 15-31. doi:https://doi.org/10.1016/j.geomorph.2004.06.010
DFDA/CRED. (2010). Annual disaster statistical review 2009. from Center for research on epidemiology of disasters http://www.cred.be/sites/ default/files/ ADSR_2009
Nadim, F., & Kjekstad, O. (2009). Assessment of global high-risk landslide disaster hotspots. Landslide- disaster risk reduction, 213-221.
Petley, D. (2012). Global patterns of loss of life from landslides. Geology, pubs. Geoscience world. Org, 40(10), 927-930.
Berberian, M. (1976). An explanatory note on the first seism tectonic map of Iran, a seism tectonic review of the country. Retrieved from.