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Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method

Year 2017, Volume: 17 Issue: 2, 307 - 324, 28.09.2017
https://doi.org/10.17475/kastorman.309474

Abstract

Abstract

Aim of study: The
present study aims to analyze the landslide susceptibility of Yamaç Basin by
applying the index method.

Area of study: Covering
an area of 78 km2, Yamaç Basin is located in the southern part of
Bingöl Province. The basin is composed of lithological agglomerate, tuff,
conglomerate and sandstone. The difference in elevation between the northern
and southern part of the basin reaches as high as 1000.00 m. Heavy
precipitation, sparse vegetation, and high levels of river density have been
observed in the basin.

Material and Methods: In this
study landslide susceptibility of Yamaç Basin was analyzed by using the
parameters of lithology, slope, aspect, normalized difference vegetation index
(NDVI), distance to river systems and curvature.

Main results:
According to the susceptibility map based on the index method, landslide
susceptibility is very high in northern, western and northwestern parts of the
basin where a land route stretches from Yamaç to Olukpınar Village.











Research highlights: Natural
and environmental factors were not evaluated during either the construction of
the first settlement or of the first road. Thus, landslides cost significant
economic losses every year. Therefore, it is urgently necessary to carry out
studies on basin-scale planning in order to cope with the damages and problems
caused by landslides.

References

  • Ayalew L., Yamagishi H., Ugawa N. 2004 Landslide susceptibility mapping using GIS-based weighted linear combination. The Case in Tsugawa Area of Agano River, Niigate Prefecture, Japan. Landslides (1):73-81.
  • Ayalew L., Yamagishi H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, (65/1-2):15-31.
  • Baeza C., Corominas J. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf. Process and Landforms, (26) :1251-1263.
  • Caniani D., Pascale S., Sdao F., Sole A. 2008. Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Natural Hazards, 45 (1). Carrara A., Cardinali M., Detti R., Guzzetti F., Pasqui V., Reichenbach P. 1991. GIS techniques and statistical models in evaluating. Earth Surface Processes and Landforms, 16.
  • Chi K., Lee K., Park N. 2002. Landslide stability analysis and prediction modeling with landslide occurrences on KOMPSAT EOC imagery. Korean Journal of Remote Sensing, (18/1):1-12.
  • Dai F C., Lee C F., Li J., Xu Z W. 2001. Assessment of landslide suspectibility on the natural terrain of lantau Island, Hong Kong. Environmental Geology, 43, 3: 381-391.
  • Dai F C., Lee C F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, (42), 65-87.
  • Donati L., Turrini M C. 2002. An objective method to rank the importance of the factors pre disposing to landslides with the GIS methodology: Application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, (63) :277-289.
  • Duman T Y., Olgun Ş, Çan T., Nefeslioğlu H A., Hamzaçebi S., Elmacı H., Durmaz S., Çörekçioğlu Ş. 2009. 1/500000 Ölçekli Türkiye Heyelan Envanteri Haritası Erzurum Paftası, Maden Tetkik Arama Enstitüsü Genel Müdürlüğü, Ankara.
  • Ercanoğlu M., Gökçeoğlu C. 2004. Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, (75) : 229-250.
  • Ermin, L., Catani F., Casagli N. 2005. Arficial neural networks applied to lansdslide susceptibility assessment. Geomorphology 66: 327-343
  • Gökçeoğlu C., Aksoy H. 1996. Landslide susceptibility mapping of the slopes in the residuel soils of the Mengen Region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology, (44):147-161
  • Gómez H., Kavzoglu T. 2005. Assessment of shallow landslides susceptibility using artificial neural networks in Jabonosa river basin, Venezuela. Engineering Geology, (78) : 11-27.
  • Guzzetti F., Carrara A., Cardinalli M., Reichenbach P. 1999. Landslide hazard evaluation: A review of current techniques and their application in a Multi-Scale Study, Central Italy. Geomorphology, (31):181-216
  • Lee S., Ryu J., Won J., Park H. 2004. Determination and application of the weight for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71-80.
  • Saha A K., Gupta R P., Arora M K. 2002. GIS-based landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int. J. Remote Sensing, (23/2):357-369.
  • Sümengen M. 2011. 1/100000 Ölçekli Türkiye Jeoloji Haritaları, Elazığ K44 Paftası. MTA Genel Müdürlüğü Jeoloji Etütleri Dairesi, Ankara.
  • Tonbul S. 1990a. Bingöl ovası ve çevresinin jeomorfolojisi ve gelişimi. Coğrafya Araştırmaları Dergisi, Ankara, (2/2): 329-352.
  • Tonbul S. 1990b. Bingöl Ovası ve çevresinin iklimi. Fırat Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (4/1): 263-314.
  • Yalçın A. 2007. Heyelan duyarlılık haritalarının üretilmesinde analitik hiyerarşi yönteminin ve CBS’ nin Kullanımı. Selçuk Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, (23/23) : 1-14.
  • Yalçın A., Reis S., Aydınoğlu A C., Yomralıoğlu T A. 2011. GIS-based comparative study of frequency ratio, analytical Hierarchy process, "bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon NE Turkey. CATENA, June, Vol. 85, 274–287
  • Van Westen C J. 1993. Application of geographic information systems to landslide hazard zonation. ITC Publication no: 15. International Institute for Aerospace and Earth Resources Survey, Enschede, The Netherlands, 245.
  • Van Westen, C J. 1997. Statistical landslide hazard analysis, ILWIS 2.1 for Windows Application Guide. ITC Publication, Enschede, 73-84.

İndeks metodu ile Yamaç Havzası’nın (Bingöl) heyelan duyarlılık analizi

Year 2017, Volume: 17 Issue: 2, 307 - 324, 28.09.2017
https://doi.org/10.17475/kastorman.309474

Abstract

Özet

Çalışmanın
Amacı
: Bu çalışmada İndeks Metodu
kullanılarak Yamaç Havzası için heyelan duyarlılık analizlerinin yapılması
amaçlanmıştır.

Çalışma
Alanı
: Yamaç Havzası, Bingöl’ün
güneyinde yer almakta olup, 78 km2 alan kaplamaktadır. Kuzeyi ile güneyi
arasında yükselti farkının 1000 m’yi bulduğu havzada litoloji aglomera, tüf,
konglomera ve kumtaşından oluşmaktadır. Yağış miktarının fazla olduğu inceleme
alanında, bitki örtüsü seyrek olup, akarsu yoğunluğu yüksektir. Doğal
koşulların elverişli olması nedeniyle meydana gelen heyelanlar önemli ekonomik
kayıplara yol açmaktadır.

Materyal
ve Yöntem
: Bu çalışmada litoloji, eğim,
bakı, Normalize Fark Bitki İndeksi (NDVI), akarsu ağlarına uzaklık ve yamaç
eğriselliği parametreleri kullanılarak Yamaç Havzası için heyelan duyarlılık
analizleri yapılmıştır. İndeks metodu kullanılarak oluşturulan duyarlılık
haritasına göre havzanın kuzeyi, kuzeybatısı ve batısında heyelan duyarlılığı
oldukça yüksek olup, Yamaç ve Olukpınar köyleri arasında karayolu duyarlılığın
yüksek olduğu alandan geçirilmiştir.









Araştırma
vurguları
: Yörede yerleşmeler kurulurken ve
yollar yapılırken doğal çevre koşulları dikkate alınmamıştır. Bu nedenle
heyelanlar her yıl önemli ekonomik kayıplara neden olmaktadır. Heyelanların
meydana getireceği sorunları azaltmak için havza ölçeğinde planlama
çalışmalarının hızlandırılması gerekmektedir.

References

  • Ayalew L., Yamagishi H., Ugawa N. 2004 Landslide susceptibility mapping using GIS-based weighted linear combination. The Case in Tsugawa Area of Agano River, Niigate Prefecture, Japan. Landslides (1):73-81.
  • Ayalew L., Yamagishi H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, (65/1-2):15-31.
  • Baeza C., Corominas J. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surf. Process and Landforms, (26) :1251-1263.
  • Caniani D., Pascale S., Sdao F., Sole A. 2008. Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Natural Hazards, 45 (1). Carrara A., Cardinali M., Detti R., Guzzetti F., Pasqui V., Reichenbach P. 1991. GIS techniques and statistical models in evaluating. Earth Surface Processes and Landforms, 16.
  • Chi K., Lee K., Park N. 2002. Landslide stability analysis and prediction modeling with landslide occurrences on KOMPSAT EOC imagery. Korean Journal of Remote Sensing, (18/1):1-12.
  • Dai F C., Lee C F., Li J., Xu Z W. 2001. Assessment of landslide suspectibility on the natural terrain of lantau Island, Hong Kong. Environmental Geology, 43, 3: 381-391.
  • Dai F C., Lee C F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, (42), 65-87.
  • Donati L., Turrini M C. 2002. An objective method to rank the importance of the factors pre disposing to landslides with the GIS methodology: Application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology, (63) :277-289.
  • Duman T Y., Olgun Ş, Çan T., Nefeslioğlu H A., Hamzaçebi S., Elmacı H., Durmaz S., Çörekçioğlu Ş. 2009. 1/500000 Ölçekli Türkiye Heyelan Envanteri Haritası Erzurum Paftası, Maden Tetkik Arama Enstitüsü Genel Müdürlüğü, Ankara.
  • Ercanoğlu M., Gökçeoğlu C. 2004. Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, (75) : 229-250.
  • Ermin, L., Catani F., Casagli N. 2005. Arficial neural networks applied to lansdslide susceptibility assessment. Geomorphology 66: 327-343
  • Gökçeoğlu C., Aksoy H. 1996. Landslide susceptibility mapping of the slopes in the residuel soils of the Mengen Region (Turkey) by deterministic stability analyses and image processing techniques. Engineering Geology, (44):147-161
  • Gómez H., Kavzoglu T. 2005. Assessment of shallow landslides susceptibility using artificial neural networks in Jabonosa river basin, Venezuela. Engineering Geology, (78) : 11-27.
  • Guzzetti F., Carrara A., Cardinalli M., Reichenbach P. 1999. Landslide hazard evaluation: A review of current techniques and their application in a Multi-Scale Study, Central Italy. Geomorphology, (31):181-216
  • Lee S., Ryu J., Won J., Park H. 2004. Determination and application of the weight for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71-80.
  • Saha A K., Gupta R P., Arora M K. 2002. GIS-based landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int. J. Remote Sensing, (23/2):357-369.
  • Sümengen M. 2011. 1/100000 Ölçekli Türkiye Jeoloji Haritaları, Elazığ K44 Paftası. MTA Genel Müdürlüğü Jeoloji Etütleri Dairesi, Ankara.
  • Tonbul S. 1990a. Bingöl ovası ve çevresinin jeomorfolojisi ve gelişimi. Coğrafya Araştırmaları Dergisi, Ankara, (2/2): 329-352.
  • Tonbul S. 1990b. Bingöl Ovası ve çevresinin iklimi. Fırat Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (4/1): 263-314.
  • Yalçın A. 2007. Heyelan duyarlılık haritalarının üretilmesinde analitik hiyerarşi yönteminin ve CBS’ nin Kullanımı. Selçuk Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, (23/23) : 1-14.
  • Yalçın A., Reis S., Aydınoğlu A C., Yomralıoğlu T A. 2011. GIS-based comparative study of frequency ratio, analytical Hierarchy process, "bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon NE Turkey. CATENA, June, Vol. 85, 274–287
  • Van Westen C J. 1993. Application of geographic information systems to landslide hazard zonation. ITC Publication no: 15. International Institute for Aerospace and Earth Resources Survey, Enschede, The Netherlands, 245.
  • Van Westen, C J. 1997. Statistical landslide hazard analysis, ILWIS 2.1 for Windows Application Guide. ITC Publication, Enschede, 73-84.
There are 23 citations in total.

Details

Journal Section Articles
Authors

Alaaddin Yüksel

Vedat Avci

Publication Date September 28, 2017
Published in Issue Year 2017 Volume: 17 Issue: 2

Cite

APA Yüksel, A., & Avci, V. (2017). Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method. Kastamonu University Journal of Forestry Faculty, 17(2), 307-324. https://doi.org/10.17475/kastorman.309474
AMA Yüksel A, Avci V. Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method. Kastamonu University Journal of Forestry Faculty. September 2017;17(2):307-324. doi:10.17475/kastorman.309474
Chicago Yüksel, Alaaddin, and Vedat Avci. “Landslide Susceptibility Analysis of Yamaç Basin in Bingol through the Index Method”. Kastamonu University Journal of Forestry Faculty 17, no. 2 (September 2017): 307-24. https://doi.org/10.17475/kastorman.309474.
EndNote Yüksel A, Avci V (September 1, 2017) Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method. Kastamonu University Journal of Forestry Faculty 17 2 307–324.
IEEE A. Yüksel and V. Avci, “Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method”, Kastamonu University Journal of Forestry Faculty, vol. 17, no. 2, pp. 307–324, 2017, doi: 10.17475/kastorman.309474.
ISNAD Yüksel, Alaaddin - Avci, Vedat. “Landslide Susceptibility Analysis of Yamaç Basin in Bingol through the Index Method”. Kastamonu University Journal of Forestry Faculty 17/2 (September 2017), 307-324. https://doi.org/10.17475/kastorman.309474.
JAMA Yüksel A, Avci V. Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method. Kastamonu University Journal of Forestry Faculty. 2017;17:307–324.
MLA Yüksel, Alaaddin and Vedat Avci. “Landslide Susceptibility Analysis of Yamaç Basin in Bingol through the Index Method”. Kastamonu University Journal of Forestry Faculty, vol. 17, no. 2, 2017, pp. 307-24, doi:10.17475/kastorman.309474.
Vancouver Yüksel A, Avci V. Landslide susceptibility analysis of Yamaç Basin in Bingol through the index method. Kastamonu University Journal of Forestry Faculty. 2017;17(2):307-24.

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