Geomorphology Planning Zonation for the Development of Residential Areas Based on the Fuzzy Model (A Case Study of North and East Yazd Province)

جغرافیاوتوسعه ناحیه ای

Article 8, Volume 15, Issue 2 - Serial Number 29, March 2018, Pages 173-196   

Document Type: scientific-research article

DOI: 10.22067/geography.v15i2.63301

Authors

Masih Moradizadeh¹; Kourosh Shirani^{* 2}; Abbas Alipour³; Danial Dabiri⁴; Seyed Mostafa Hashemi⁵

¹University of Tehran

²Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan

³Imam Hosein University

⁴University of National defense, Tehran

⁵Islamic Azad University, Science and Research Branch, Tehran

Abstract

Extended abstract
1. Introduction
Traditional style of life and geomorphologic unfamiliarity in coping with environment has made human communities to search for new places to dominate and utilize its potential for settlement. Thus, it can be said that such settlements were initially based on natural potential and in a close relation with it. Stuyding the natural environment of these settlements is important because their development depends on their natural condition. While the rapid development of cities is an undeniable fact, determining the proper places for cities expansion is critical. While most of cities are growing fast and have a physical development process, identifying suitable sites for development is essential. To control and guide the development of urban and rural areas to determine the optimal development, directions, locations, and appropriate protective measures are necessary. In order to control and direct city expansion, it is vital to define the appropriate orientation of the development, proper sites, and protection practices. The facing problem is that which places are suitable for settlement expansion according to their resistancy condition. Consequently, this paper aims to delineate sites with high potential of settlement development.
2. Theoretical Framework
Data were analyzed by Fuzzy Logic and geomorphologic planning techniques. In the Fuzzy set, zero means any member doesn't exist in the set and one means all the members exist in the set. The AND, OR, Product, Sum, and Gamma functions are used in modeling.
3. Methodology
In order to provide the layers of the desired parameters, first of all, the desired images were geo-referenced using the same system (UTM: WGS 1984, Zone 40N) and layers with common pixel size of 30 m were produced. Information resources used in this study consisted of written documents and statistical data. (Visual documentation and interviews are shown in a flow chart. The data included topographic maps (1/50000, 1/250000), geology (1/250000, 1/100000), soil map (1/250000), land use (1/250000), and air lab (1/55000). Digital resources included digital elevation model (DEM) 30 meters and TM's satellite images. Also, much of the information related to the area such as lithology and faults, soil, land use, hypsometric, political subdivisions maps, towns and villages, a variety of maps, aerial photos and satellite images, and DEM were extracted. In an attempt to determine proper places for settlement expansion, several environmental parameters were selected including slope degree and aspect, altitude, geology, soil, land use, distance to fault, distance to river, distance to road, distance to nearby settlement, and geomorphology. A digitized layer was prepared for each parameter using GIS technique. Available maps, layers, and images were initially georeferenced by using the same georeference system that is UTM: WGS 1984, zone 40N. Produced layers have the same pixel size of 30 m2. Data were then analyzed by fuzzy logic and geomorphologic planning techniques.
4. Results and Discussion
Determined parameters were overlayed in fuzzy models with gamma=0.9 as the best criteria. Using natural fracture method, the generated map was then classified in five groups of totally inappropriate (0-0.184), inappropriate (0.184-0.332), average (0.332-0.498), proper (0.498-0.625), very suitable (0.625-0.986). The group named totally inappropriate with 72.79% has the maximum area. Following it, proper group with 9.92% has the second ranking of area. Inappropriate, proper, and average groups have 1.18, 7.25 and 8.85 percent area respectively. Comparing these points with the final map shows that the most settlement sites are located in the average group. However, the least numbers of settlement points are found in inappropriate group. However, these few sites were remained abandoned in recent decades due to the unfavorable environmental condition.
5. Conclusion and Suggestions
Results show that the assessment of factors affecting the forming and trend of groups has a main role in determining proper or inappropriate sites for human settlement. According to the final zoning map, south and south east of the study area are suitable for settlement expansion. Also, most settlement sites were located in the average group, while the least numbers of settlement sites were found in the inappropriate group. A typical example of such unfavorable environmental condition is Kheirabad village which was abandoned due to desertification problems. Results show that inappropriate sites were seen in mountains, playa, clay valley, badlands, and sand dunes units. These areas do not have an acceptable condition for development due to steep slopes, rugged topography, badlands, and lithology. Proper areas for development match with alluvial fans and hillsides that have gentle slays, smooth topography, fertile soil, and so on.

Keywords

Geomorphology; Planning; Residential development; Fuzzy logic

References

1. اسفندیاری، م.(1392). نقش عوامل ژئومورفولوژیک در توسعة فیزیکی شهر اراک. (پایان‌نامة منتشرنشدة کارشناسی‌ارشد جغرافیای طبیعی)، دانشگاه شهید بهشتی، تهران، ایران.

2. پناهی حسین‌آبادی، ر. (1391). بررسی محدودیت‌های ژئومورفولوژیک توسعة فیزیکی شهر کرمانشاه. (پایان‌نامة منتشرنشدة کارشناسی‌ارشد جغرافیای طبیعی)، دانشگاه شهید بهشتی، تهران، ایران.

3. پورجعفر، م. ر.، منتظرالحجه، م.، و رنجبر، ا. (1391). ارزیابی توان اکولوژیکی به‌منظور تعیین عرصه‌های مناسب در محدودة شهر جدید سهند. مجلة جغرافیا و توسعه، 3(28)، 28-11.

4. تقیان، ع. ر.، و غلام‌حیدری، ح. (1392). پتانسیل و موانع ژئومورفولوژیکی توسعة فیزیکی شهر یاسوج با استفاده از مدل AHP. ژئومورفولوژی کاربردی ایران، 27(1)، 115-99.

5. جباری، ا. و روستایی، ش. (1387). ژئومورفولوژی مناطق شهری. انتشارات سمت.

6. رجایی، ع. ا. (1382). کاربرد ژئومورفولوژی در آمایش سرزمین و مدیریت محیط. تهران: انتشارات قومس.

7. رضایی‌مقدم، م. ح.، و ثقفی، م. (1384). کاربرد تکنیک‌های جدید برای طبقه‌بندی و تحلیل مخاطرات ژئومورفولوژی در گسترش شهر تبریز. مدرس علوم انسانی، 18(9)، 32-18.

8. زمردیان، م. ج. (1383). کاربرد جغرافیای طبیعی در برنامه‌ریزی شهری و روستایی. تهران: انتشارات سمت.

9. زیاری، ک. ‌ا. (1378). برنامه‌ریزی شهرهای جدید. تهران: انتشارات سمت.

10. شایان، س.، و پرهیزگار، ا. س. ش. (1388). تحلیل امکانات و محدودیت‌های ژئومورفولوژیک در انتخاب محورهای توسعة شهری شهر داراب. برنامه‌ریزی و آمایش فضا (مدرس علوم انسانی) ،25(13)، 58-42.

11. صفاری، ا. (1387). قابلیت و محدودیت‌های ژئومورفولوژیکی کلان‌شهر تهران به‌منظور توسعه و ایمنی. (رسالة دکتری رشتة جغرافیای طبیعی ژئومورفولوژی)، دانشگاه تهران، تهران، ایران.

12. عزیزی، ع.‌ ا. (1382). سنجش و توسعة روستایی و شناسایی روستاهای مرکزی به‌منظور ارائة الگوی سلسله‌مراتبی مناسب خدمات‌رسانی در روستاهای بخش فراهان نقرش. (پایان‌نامة منتشرنشدة کارشناسی ‌ارشد کشاورزی)، دانشگاه تهران، تهران.

13. عیسی‌پور، ر.، و مجد رحیم‌آبادی، م. (1394). تحلیل عوامل مؤثر در توسعة فیزیکی شهر رحیم-آباد (شهرستان رودسر) طی دو دهة اخیر. فصلنامه برنامه‌ریزی منطقه‌ای،18(17)، 57-42.

14. مختاری، د.، و امامی‌کیا، و. (1393). پهنه‌بندی کاربری اراضی شهری شهرک ارم تبریز براساس شاخص‌های اساسی مخاطرات ژئومورفولوژیک، آمایش جغرافیایی فضا، 25(4)، 172-149.

15. مقیمی، ا. (1385). ژئومورفولوژی شهری. تهران: انتشارات دانشگاه تهران.

16. مقیمی، ا.، و صفاری، ا. (1389). ارزیابی ژئومورفولوژیکی توسعة شهری در قلمرو حوضه‌های زهکشی سطحی کلان‌شهر تهران. مجلة علوم جغرافیایی،34( 14)، 39-21.

17. ملکی، ا. (1388). ارزیابی موقعیت مکانی استقرار اماکن روستایی در شهرستان کرمانشاه. فصلنامة پژوهشی جغرافیای انسانی، 35(3)، 35-17.

18. Anabstani, G. (2011). Naghsh avamel tabiee dar payedari sokounatgahaye roostaeei (shahr Sabzevar) [The role of natural factors in stability of rural settlements (Case study: Sabzevar county)]. Geography and Environmental Planning, 40(4), 89-104.

19. Awasthi, A., Chauhan, S. S., & Goyal, S. K. (2011). A multi-criteria decision-making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53, 98–109.

20. Ayala, I. (2002). Geomorphology, natural hazard, vulnerability and prevention of natural geomorphology: Natural disasters in developing countries. Geomorphology, 1(47), 107-124.

21. Baz, I., Geymen, A., & Nogay Er, S. (2010). Development and application of GIS-based analysis synthesis modeling techniques for urban planning of Istanbul metropolitan area. Journal Advances in Engineering Software, 40(2), 128-140.

22. Dadras, M., Shafri, H. Z. M., Ahmad, N., Pradhan, B., & Safarpour, S. (2014). A combined fuzzy MCDM approach for identifying the suitable lands for urban development: An example from Bandar Abbas. Journal of Urban and Environmental Engineering, 8(1), 11-27.

23. Foroutan, E., & Delavar, M. R. (2012, March). Urban growth modeling using fuzzy logic. Paper presented at the ASPRS 2012 Annual Conference on Fuzzy Systems . Sacramento, California.

24. Gresswell R.E, (2013). Spatoal and temporal patterns of debrise-flow deposition in the Oregon coast ange,U.S.A. Geomorphology, 2(57), 59-70.

25. Gutman, G., Janetos, A. C., Justice, C. O., Moran, E. F., Mustard, J. F., Rindfuss, R. R., …, & Cochrane, M. A. (Eds.). (2004). Land change science: Observing, monitoring and understanding trajectories of change on the earth’s surface. New York: Kluwer Academic Publishers.

26. Hashemi, N., & Rostami, M. (2015). The prioritization of urban regions towards developing green spaces (parks) through GIS (A case study of the 3rd division of the metropolis of Kermanshah-Iran). Journal of Applied Environmental and Biological Sciences, 5(1),186-195.

27. Juang, C., Lee, D., & Sheu, H. (1992). Mapping slope failure potential using fuzzy sets. Journal of Geotechnical Engineering, 118(3), 475-486.

28. Kanungo, D. P., Arora, M. K., Sarkar, S., & Gupta, R. P. (2006). A comparative study of conventional, ANN, black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in darjeling Himalayas. Engineering Geology, 43(3), 65-73.

29. Klir, G. J. (1994). Multivalued logics versus modal logics: Alternative frameworks for uncertainty modelling. In P. P. Wang (Ed.), Advances in fuzzy theory and technology (pp. 3-47). Durham: Duke University.

30. Kosko, B. (1992, 8-12 March ). Fuzzy systems as universal approximators fuzzy systems. Paper presented at the International Conference on Fuzzy Systems, Institute of Electrical and Electronics Engineers (IEEE), San Diego, CA.

31. Kuswandari, R. (2004). Assessment of different methods for measuring the sustainability of forest management. International institute for geo-information science and earth observation, Enscheda, Netherlands.

32. Leon‚ J., & March‚ A. (2014). Urban morphology as a tool for supporting tsunami rapid resilience: A case study of Talcahuano, Chile. Habitat International‚ 43, 250–262.

33. Panizza, M. (1997). Geomorphology, natural hazard in the vulnerability and prevention of natural disasters in developing countries. Geomorphology, 47, 107-124.

34. Rydin, Y. (2003). Conflict, consensus and rationality in environmental planning: An institutional discourse approach. Oxford University Press, Oxford.

35. Shenavr, B., & Hosseini, S. M. (2014). Comparison of multi-criteria evaluation (AHP and WLC approaches) for land capability assessment of urban development in GIS. International Journal of Geomatics and Geoscience, 4(3), 251-262.

36. Thapa, B. R., & Muryama, Y. (2009). Examining spatiotemporal urbanization patterns in Kathmandu Valley, Nepall: Remote sensing and spatial metrics approaches. Journal of Remote Sensing, 1, 534-556.

37. Tolga, E., Demircan, L., & Kahraman, C. (2005). Operating system selection using fuzzy replacement analysis and analytic hierarchy process. Journal Production Economics, 97(1), 122-134.

38. United Nations Office for Disaster Risk Reduction (UNISDR) (2010). Final report disaster risk reduction in Iran.

39. Zaddeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

40. Zaddeh, L. A. (1996). Fuzzy logic: Computing with words. Institute of Electrical and Electronics Engineers (IEEE), 4(4) , 9-16.