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Showing 2 results for Time Series Analysis
Mohammad Ali Moradi,
Volume 10, Issue 2 (7-2010)
Volume 10, Issue 2 (7-2010)
Abstract
Since the first oil shock in 1973, almost the economic performance of Iran has been related to its natural resource wealth. The economy has experienced relatively lower per capita GDP growth and higher income inequality. This may support this hypothesis that natural resources seem to have been more of a curse than a blessing for Iran. This paper aims to analyze the effects of oil resource abundance on two major macroeconomic variables, economic growth and income distribution, in Iran using the data over the period 1968 - 2005. I take a time series perspective and focus on major forces of economic growth including oil resource. Moreover, the main determinants of income distribution are theoretically specified to examine the effects of oil resource. Due to the problem of data availability, and ARDL approach is employed to estimate the empirical models. Using the production function approach, the results of the study confirm that the overall long run effect of oil abundance on GDP is positive and significant but the value of the estimated coefficient is too small. The findings show that physical capital and human capital have positive and significant effects on GDP in the long run. Moreover, this study finds that oil abundance have negative and significant effect on income distribution. It means that oil revenue improves income equality in Iran. It should be point out that while the Gini coefficient is relatively higher compared to most countries, poverty level are substantially lower because of the distinguished social welfare system in the country and cohesive system of private social responsibility through a religious charitable system. However, income and human capital have a negative and significant effect on income distribution. The overall findings appeared to support this hypothesis that oil abundance is not a blessing for Iran.
Volume 25, Issue 2 (7-2021)
Abstract
Nowadays, given the rapid growth of population, development of infrastructure is inevitable and the pressure of human needs on the soil and exploitation of areas around cities in rural areas are increasing. Access to surface water, fertile soil, groundwater, access to transit roads, etc. have made establishing of new cities compulsory despite the environmental hazards in those areas.
Land deformation as an environmental hazard may be related to tectonic activities such as earthquakes, faults, volcanoes, landslides and anthropogenic processes such as groundwater exploitation, which threaten urban areas. Land surface subsidence is recognized as a potential problem in many areas. This phenomenon is most often caused by human activities, mainly from the removal of subsurface water. Also, Iran with rough and mostly mountainous topography, have a high potential for landslides and instability of slopes.
Pardis new city in the east part of Tehran is one of the areas most prone to Domain Instabilities. The location of the city and its expansion toward the steep slopes have made it susceptible to all kinds of natural hazards, so the main purpose of the study is investigate the potential of landslide and subsidence in Pardis.
Material and Methods
This research consists of two stages: first, ground surface deformation was estimated using radar interferometry technique. Then, landslide susceptible zoning was carried out using Fuzzy and AHP methods.
We applied SBAS algorithm to the 27 SAR images of the Sentinel-1 satellite, in ascending orbit for the time period of 2016.01.06.-2018.12.21. The first step of the SBAS procedure involves the selection of the SAR data pairs to generate the interferograms; the selected images are characterized by a small temporal and spatial separation (baseline) between the orbits in order to limit the noise effect usually referred to as decorrelation phenomena. The second step of the procedure involves the retrieval of the original (unwrapped) phase signals from the modulo-2 π restricted (wrapped) phases directly computed from the interferograms.
In the next stage, landslide susceptibility zones have been evaluated using both fuzzy logic and analytical hierarchy process (AHP) models, as a weighting technique to explore landslide susceptibility mapping. In the modelling process, eight causative variables including aspect, slope degree, altitude, distance from the road, distance from the fault, distance from the river, lithology and land use were identified for landslide susceptibility mapping.
In fuzzy logic the degree of membership of variables may be any real number from 0 (non-membership) to 1 (full membership) which reflects a degree of membership (Zadeh, 1965). By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. After Fuzzification of all layers, since the causative factors are not the same value, the AHP method to determine the weights was performed. The AHP methodology consists of pairwise comparison of all possible pairs of factors. The relative rating for the dominance between each pair of factors was guided by expert knowledge. After obtaining weight of each factors, these weights are multiplied in the map calculated by fuzzy membership.
Results and Discussion
We used 27 c-band sentinel-1 images for the 2016-2018 period and the Small BAseline Subset (SBAS) approach to investigate land deformation in Pardis. Result of the deformation map of Pardis show that the northern part is uplifted with an annual rate of 25 mm/yr. The uplift of the northern part can be attributed to tectonic factors and the southern part of the basin subsided with an annual rate of -35 mm/yr. Thereafter landslide susceptibility areas have been evaluated. Geomorphological variables (slope, aspect, elevation, river), geology variables (lithology, fault) and anthropogenic variables (land use, roads) have been used for generation of the landslide susceptible map. The results of the landslide susceptible map indicate that the northern part of the Pardis basin have a high potential for landslides. Landslide susceptible map is classified into five classes: very high, high, medium, low and very low.
Medium to very high susceptible class covered 40% of the study area which overlay on uplifting areas resulting from radar technique.
Conclusion
SBAS time series method has been used to detect ground surface deformation and vertical movements. This method is based on an appropriate combination of multi look DInSAR Interferograms. Deformation map indicate that northern part of the basin, uplifted and southern part subsided. The cities of Pardis, Roodehen and Boomehen in the southern part, subsided a mean rate of respectively -35, -31 and -29 mm/year. The northern part uplifted with a mean rate of 25 mm/year which can be attributed to tectonic activity. Then, the landslide susceptibility map was created using both Fuzzy and AHP methods. The result show that more than 40% of the basin is exposed to landslides. The results of both methods SBAS time series analysis, landslide susceptibility mapping, demonstrated domain instabilities in northern part of the basin. As a result, identifying instable areas seems necessary for the urban development of the Pardis.
Key words: Pardis city, SBAS time series analysis, landslide, subsidence
Land deformation as an environmental hazard may be related to tectonic activities such as earthquakes, faults, volcanoes, landslides and anthropogenic processes such as groundwater exploitation, which threaten urban areas. Land surface subsidence is recognized as a potential problem in many areas. This phenomenon is most often caused by human activities, mainly from the removal of subsurface water. Also, Iran with rough and mostly mountainous topography, have a high potential for landslides and instability of slopes.
Pardis new city in the east part of Tehran is one of the areas most prone to Domain Instabilities. The location of the city and its expansion toward the steep slopes have made it susceptible to all kinds of natural hazards, so the main purpose of the study is investigate the potential of landslide and subsidence in Pardis.
Material and Methods
This research consists of two stages: first, ground surface deformation was estimated using radar interferometry technique. Then, landslide susceptible zoning was carried out using Fuzzy and AHP methods.
We applied SBAS algorithm to the 27 SAR images of the Sentinel-1 satellite, in ascending orbit for the time period of 2016.01.06.-2018.12.21. The first step of the SBAS procedure involves the selection of the SAR data pairs to generate the interferograms; the selected images are characterized by a small temporal and spatial separation (baseline) between the orbits in order to limit the noise effect usually referred to as decorrelation phenomena. The second step of the procedure involves the retrieval of the original (unwrapped) phase signals from the modulo-2 π restricted (wrapped) phases directly computed from the interferograms.
In the next stage, landslide susceptibility zones have been evaluated using both fuzzy logic and analytical hierarchy process (AHP) models, as a weighting technique to explore landslide susceptibility mapping. In the modelling process, eight causative variables including aspect, slope degree, altitude, distance from the road, distance from the fault, distance from the river, lithology and land use were identified for landslide susceptibility mapping.
In fuzzy logic the degree of membership of variables may be any real number from 0 (non-membership) to 1 (full membership) which reflects a degree of membership (Zadeh, 1965). By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. After Fuzzification of all layers, since the causative factors are not the same value, the AHP method to determine the weights was performed. The AHP methodology consists of pairwise comparison of all possible pairs of factors. The relative rating for the dominance between each pair of factors was guided by expert knowledge. After obtaining weight of each factors, these weights are multiplied in the map calculated by fuzzy membership.
Results and Discussion
We used 27 c-band sentinel-1 images for the 2016-2018 period and the Small BAseline Subset (SBAS) approach to investigate land deformation in Pardis. Result of the deformation map of Pardis show that the northern part is uplifted with an annual rate of 25 mm/yr. The uplift of the northern part can be attributed to tectonic factors and the southern part of the basin subsided with an annual rate of -35 mm/yr. Thereafter landslide susceptibility areas have been evaluated. Geomorphological variables (slope, aspect, elevation, river), geology variables (lithology, fault) and anthropogenic variables (land use, roads) have been used for generation of the landslide susceptible map. The results of the landslide susceptible map indicate that the northern part of the Pardis basin have a high potential for landslides. Landslide susceptible map is classified into five classes: very high, high, medium, low and very low.
Medium to very high susceptible class covered 40% of the study area which overlay on uplifting areas resulting from radar technique.
Conclusion
SBAS time series method has been used to detect ground surface deformation and vertical movements. This method is based on an appropriate combination of multi look DInSAR Interferograms. Deformation map indicate that northern part of the basin, uplifted and southern part subsided. The cities of Pardis, Roodehen and Boomehen in the southern part, subsided a mean rate of respectively -35, -31 and -29 mm/year. The northern part uplifted with a mean rate of 25 mm/year which can be attributed to tectonic activity. Then, the landslide susceptibility map was created using both Fuzzy and AHP methods. The result show that more than 40% of the basin is exposed to landslides. The results of both methods SBAS time series analysis, landslide susceptibility mapping, demonstrated domain instabilities in northern part of the basin. As a result, identifying instable areas seems necessary for the urban development of the Pardis.
Key words: Pardis city, SBAS time series analysis, landslide, subsidence
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