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Record W2992105943

Site selection of the suitable areas for the physical development of Tehran megalopolis based on the climatic elements and geographic factors

2012· article· en· W2992105943 on OpenAlexaboutno aff
Firouz Mojarrad, Seyed Hossein Hoseinifar

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicRemote Sensing and Land Use
Canadian institutionsnot available
Fundersnot available
KeywordsMegalopolisSelection (genetic algorithm)PersianGeographyEnvironmental planningComputer scienceEconomic geographyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Extended abstract1- Introduction Developing the cities and it’s influences on the spatial structure of megalopolises has always been one of the most important factors for the planners. Among the most important factors directing the development of the cities, are natural factors such as climatic ones which have been less considered in the country as yet because of various factors like economic benefits resulting from immethodical construction of the buildings. Since this carelessness has caused an unwise and easy-going development of Tehran megalopolis in unsuitable geographic directions, so this research intends to accomplish the optimum site selection for physical development of Tehran megalopolis based on climatic elements and geographic factors. 2- MethodologyTopographic maps of the region which mainly consist some parts of Tehran and Alborz provinces on a scale of 1:250,000 were prepared and the region boundary was defined on them. In the next step, the climatic data of six meteorological stations was taken from the Iranian Meteorological Organization and, after reconstruction, was considered through the similar time range of 1984 to 2005. Then, using ArcGIS software and on the basis of the climatic and geographic factors, various layers affecting the site selection including topography, digital elevation model (DEM), slope percent and aspect, solar radiation angle, temperature means (minimum, maximum and daily), mean diurnal temperature difference, 24 hrs. maximum precipitation, and mean wind speed were made and entered into the software. Pixels’ values in the climatic layers were calculated via spline interpolation method or regression equations. Then the resulted raster layers, after the reclassification of their pixels’ values, were weighed and overlayed using Spatial Analyst (SA) and Spatial Analytical Hierarchy Process (SAHP) models and thus the final maps of the physical development suitability of these two models were obtained.On the other hand, two Landsat ETM-7 images prepared from Iranian Space Agency and some geometric corrections were made on them using PCI (Geomatica) software in UTM WGS84 projection. Before geometric corrections, because of temporal differences of images, radiometric corrections were made on them too. radiometric correction or normalization means the reconstruction of image values so that there is a linear and similar relation between pixels and their real radiations in the whole imaging area. The result was the Normalized Difference Vegetation Index (NDVI) map and the land use map. As the last step, the final site selection maps were presented via overlaying the NDVI and the land use maps with final maps of each two SA and SAHP models.3– DiscussionBased upon the distribution of the development suitability zones in the site selection maps, the most unfavorable areas have been developed in the high slope sections of the north of the region in the SA model. According to this model, about 8500 km2 of the region has not any special limitation for the development. In the SAHP model too, the most important limitation in the way of the development is the high slope sections of north of the region. The most suitable areas with almost 1400 km2 area stands in the south, west and the submontanes of north and northwest of the region. Partly suitable areas with the area equal to 5300 km2 have been developed in the central and southern parts of the region. The comparison of the final maps of two models reveals that the most suitable areas in the SAHP model have lesser extent than in the SA model. 4– ConclusionConsidering the bare land expansion which is mainly developed in the south of region and overlaying it with suitability zones of the SA and SAHP models and in order to protect the vegetation cover specially in the western parts of the region, among the three suitable zones for physical development of the city, the south direction is the most favorable direction and the western and southwestern regions respectively stand in the next precedences. In order to get a more comprehensive study in this regard, the other natural factors such as geologic, geomorphologic, hydrologic and human factors must be taken into account too.Key words: site selection, physical development, Tehran, climate, GISReferencesAlijani, B., (2006), Climate of Iran, Payam-e Noor Univ. Pub., Tehran, pp. 221.Badr, R., (2000), the use of GIS and RS in determining the city’s physical development direction (Case Study: Razi city), M. Sc. Thesis, supervisor: Alimohammadi, Abbas, Tarbiat Modarres Univ., Tehran, Dept. of remote sensing.Bahraini, S. H., (2011), Process of urban designing, Tehran Univ. Pub., 6th Edition, Tehran, pp. 469.Bahram Soltani, K., (1992), Issues and methods of urban planning (environment), urban planning & architecture research center, Ministry of housing and urban development, 1th Edition, Tehran, pp. 240.Bahram Soltani, K., (2001), The role of climate in urban environments studies, Green Wave, 5: 18-21.Bathrellos, G. D., Gaki-Papanastassiou, K., Skilodimou, H. D., Papanastassiou, D., Chousianitis, K. G., (2011), Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters, Environ Earth Sci, published online 04 August 2011, www. springerlink. com.Burrough, P. A., (1986), Principles of Geographic Information System for Land Resources Assessment, Clarendon Press, Oxford. 193 pp.Chakhar, S., Mousseau, V., (2008), GIS-based multicriteria spatial modeling generic framework, Int J Geogr Inf Sci 22(11–12): 1159–1196.Dong, J., Zhuang, D., Xu, X., Ying, L., (2008), Integrated evaluation of urban development suitability based on remote sensing and GIS techniques—a case study in Jingjinji Area, China, Sensors , 8:5975–5986.Esbah, H., (2007), Land Use Trends During Rapid Urbanization of the City of Aydin, Turkey, Environ Manage, 39:443-459.Faraji Sabokbar, H. A., (2005), Site selection of commercial services units using AHP method (Case Study: Torghabeh District of Mashhad County), Geographical Researches, 51: 125-137. Farajzadeh, M., (2010), Principals of geographic information system, Entekhab Pub., 1th Edition, Tehran, pp. 224. Ghayoor, H. A., (1996), Flood and floody areas in Iran, Geographical Research, 40: 101-120.Khosravi, M., (2004), Determining the physical development direction of Andimeshk city using satellite data (RS) and geographic information system (GIS), M. Sc. Thesis, supervisors: Nazarian, A., Tavallaei S., Tarbiat Moallem Univ., Tehran, Dept. of Geography.Liu, Y. G., Zeng, X. X., Xu, L., Tian D. L., Zeng, G. M., Hu, X. J., Tang, Y. F., (2011), Impacts of land-use change on ecosystem service value in Changsha, China, J. Cent. South Univ. Technol., 18: 420−428.Ministry of Power, (2006), Draft of guide of flood damage evaluation, Iranian Water Resources Management Co., Issue No: 296-A, pp. 95.Mkhabela, M. S., Bullock, P., Raj, S., Wang, S., Yang, Y., (2011), Crop yield forecasting on the Canadian Prairies using MODIS NDVI data, Agricultural and Forest Meteorology, 151: 385–393.Movahedi, S., Soltanian, M. (2011), GIS and climatology, Kankash Press & Pub., 1th Volume, 1th Edition, Esfahan, pp. 188.Over, S., Buyuksarac, A., Bekta, O., Filazi, A., (2011), Assessment of potential hazard and site effect in Antakya (Hatay Province), SE Turkey, Environ Earth Sci, 62: 313–326.Rabiei Dastjerdi, H. R., (2003), Modeling the unreliability of change detection based on the satellite data classification (Case Study: Esfahan city), M. Sc. Thesis, supervisor: Ziaeian, P., Tarbiat Modarres Univ., Tehran, Dept. of remote sensing.Rahimion, A., (1998), The analysis of suitability of inside city lands using geographic information system in Baghershahr city in Tehran, M. Sc. Thesis, Tarbiat Modarres Univ., Tehran, Dept. of remote sensing.Rahnemaei, M. T., (1999), Spatial limitations of Tehran city, Geographical Researches, 34: 7-19.Saaty, T. L., (2004), Decision making–the analytic hierarchy and network processes (AHP/ANP), J Syst Sci Syst Eng, 13(1): 1–35.Saeednia, A., (1993), Location of Tehran city, Environmental researches collection, 15: 43-61.Thapa, R. B., Murayama, Y., (2010), Drivers of urban growth in the Kathmandu Valley, Nepal: examining the efficacy of the analytic hierarchy process, Appl Geogr, 30: 70–83.Tudes, S., Yigiter, N.D., (2010) Preparation of land use planning model using GIS based on AHP: case study Adana-Turkey, Bull Eng Geol Environ, 69: 235–245.Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., Huang, Z. (2006), Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing, Landsc Urban Plan, 75:69–80.Yesilnacar, E., Doyuran, V., (2000), Selection of Settlement Areas Using GIS and Statistical Method (Spatial-AHP), Middle East Technical University, Ankara.Zooeshtiagh, H., (1992), Abstract of Tehran organizing plan, Ministry of housing and urban development, Tehran.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.161
GPT teacher head0.445
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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