MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur OpenAlexaboutno aff
Firouz Mojarrad, Seyed Hossein Hoseinifar

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueRemote Sensing and Land Use
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMegalopolisSelection (genetic algorithm)PersianGeographyEnvironmental planningComputer scienceEconomic geographyArtificial intelligence
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,729

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,161
Tête enseignante GPT0,445
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueDOAJ (DOAJ: Directory of Open Access Journals)Même sujetRemote Sensing and Land UseTravaux en français237 207