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Kayma tipi heyelanların farklı duyarlılık modellerinde kombinasyonu: Sakarya Havzası Yukarı Çığırı örneği

Year 2022, Issue: 80, 21 - 38, 30.06.2022

Mehmet Emin Cihangir

https://doi.org/10.17211/tcd.1065523
Cited By: 1

Abstract

Heyelan duyarlılık haritaları heyelanın mekânsal tahmini için önem arz etmektedir. Bu nedenle
heyelan duyarlılık modellerinin doğruluğu tehlike ve risk çalışmaları için temel oluşturmaktadır.
Bir bölgede heyelanın tüm tipleri için tek bir model oluşturulması duyarlılığın başarısını etkilemektedir.
Heyelanların her bir tipi, farklı mekanizma ve materyalde gerçekleştiği için heyelanı
denetleyen hazırlayıcı koşullar da değişmektedir. Bu yüzden duyarlılık modellerinin tek bir heyelan
tipine göre oluşturulması daha iyi sonuçlar vermektedir. Bu nedenle çalışmanın amacı, tek
bir heyelan ana mekanizmasına bağlı moloz ve toprak kayması tipine göre duyarlılık haritalarının
nitel ve yarı nicel yaklaşımlarda nasıl sonuçlar verdiğini ortaya koymaktır. Bu amaç doğrultusunda
Sakarya havzasının yukarı çığırında bulunan çalışma alanı için, Varnes (1978) sınıflamasına
göre moloz ve toprak kayması tipindeki heyelanlar için Frekans Oran, Analitik Hiyerarşi Süreci,
Ağırlıklandırılmış Çakıştırma, Modifiye AHP ve CBS Matris Model yaklaşımları ile duyarlılık modelleri
oluşturulmuştur. Model sonuçlarına bağlı duyarlılık oluşturulurken heyelanın yamacın
tamamını etkileyeceğinden çalışma alanı yamaç ünitelerine bölünerek çalışılmıştır. Beş model
sonucuna göre ROC eğrisinin altında kalan sonuçlar 0,79 ile 0,92 arasında değişmektedir. Bu
durum modellerin çok iyi sonuçlar verdiğini ve çalışma sahasının heyelan duyarlılığı açısından
iyi temsil edildiğini göstermektedir. Sonuçlara göre heyelanın en yüksek ve en düşük olabileceği
alanlar tüm modelde ortak alanlara karşılık gelmektedir. Çalışmada sonuç olarak ana heyelan
tipine göre oluşturulan modellerin yüksek sonuçlar verdiği ortaya çıkmıştır. Bu sonuçlar, tüm
modelin tek bir modelde birleştirilmesinde kolaylık sağlamıştır. Böylece tüm modelden tek bir
model çıktısı elde eden çalışma, heyelan tehlike ve risk çalışmalarının iyileştirilmesine katkı sağlamaktadır.

Keywords

Heyelan Duyarlılık Modeli Yamaç Ünitesi Sakarya

Thanks

Yazar, öğretileri ve yardımları için Tolga Görüm ve Hakan Ahmet Nefeslioğlu'na teşekkür eder.

References

  • Basharat, M., Shah, H. R., & Hameed, N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 1-19.
  • Brabb, E. E., & Pampeyan, E. H. (1972). Preliminary map of landslide deposits in San Mateo County, California (No. 344). US Geological Survey.
  • Carrara, A., Carratelli E.P., Merenda L. (1977) Computer-based data bank and statistical analysis of slope instability phenomena. Zeitschrift für Geomorphologie, 21 (1977), pp. 187-222.
  • Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427-445. https://doi.org/10.1002/esp.3290160505
  • Carrara A., Cardinali M., Guzzetti F., Reichenbach P. (1995) GIS technology in mapping landslide hazard. A. Carrara, F. Guzzetti (Eds.), Geographical Information Systems in Assessing Natural Hazards, Kluwer Academic Publisher, Dordrecht, The Netherlands , pp. 135-175.
  • Casagli, N., Catani, F., Puglisi, C., Delmonaco, G., Ermini, L., & Margottini, C. (2004). An inventory-based approach to landslide susceptibility assessment and its application to the Virginio River Basin, Italy. Environmental and Engineering Geoscience, 10(3), 203-216. https://doi.org/10.2113/10.3.203
  • Cihangir, M. E., & Görüm, T. (2016). Kelkit vadisinin aşağı çığırında gelişmiş heyelanların dağılım deseni ve oluşumlarını kontrol eden faktörler. Türk Coğrafya Dergisi, (66), 19-28. https://doi. org/10.17211/tcd.84731
  • Cihangir, M. E., Görüm, T., & Nefeslioğlu, H. A. (2018). Heyelan tetikleyici faktörlerine bağlı mekânsal hassasiyet değerlendirmesi. Türk Cografya Dergisi (70), 133-142. https://doi.org/10.17211/ tcd.410998
  • Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2006). A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology, 50(7), 941-961.
  • Dagdelenler, G., Nefeslioglu, H. A., & Gokceoglu, C. (2016). Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bulletin of Engineering Geology and the Environment, 75(2), 575-590.
  • DeGraff, J. V., & Romesburg, H. C. (2020). Regional landslide susceptibility assessment for wildland management: a matrix approach. Routledge. Coates D. , Vitek J. (Eds.), Thresholds in Geomorphology, George Allen and Unwin, London (1980), pp. 401-414.
  • Du, J., Glade, T., Woldai, T., Chai, B., & Zeng, B. (2020). Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Engineering Geology, 270, p.105572. https://doi.org/10.1016/j.enggeo. 2020.105572
  • Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M., & Poesen, J. (2009). Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Natural Hazards and Earth System Sciences, 9(2), 507-521.
  • Erener, A., & Düzgün, H. S. B. (2010). Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7(1), 55-68. https://doi.org/10.1007/s10346-009-0188-x
  • Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3- 4), 99-111.
  • Fernández, C. I., Del Castillo, T. F., Hamdouni, R. E., & Montero, J. C. (1999). Verification of landslide susceptibility mapping: a case study. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 24(6), 537- 544. https://doi.org/10.1002/(SICI)1096-9837(199906)24:6%- 3C537::AID-ESP965%3E3.0.CO;2-6
  • Gorum, T., Gonencgil, B., Gokceoglu, C., & Nefeslioglu, H. (2008). Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: The Melen Gorge (NW Turkey). Natural hazards, 46(3), 323-351.
  • Guzzetti, F. (2021). Invited perspectives: Landslide populations–can they be predicted?. Natural Hazards and Earth System Sciences, 21(5), 1467-1471.
  • Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1-4), 181-216.
  • Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1-2), 166-184. https://doi.org/10.1016/j. geomorph.2006.04.007
  • Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1), 272-299. https://doi.org/ 10.1016/j.geomorph.2005.06.002
  • Hansen, A., (1984). Landslide hazard analysis. In: Brunsden, D., Prior, D.B. (Eds.), Slope Instability. Wiley and sons, New York, pp. 523–602.
  • Irigaray, C., Fernández, T., El Hamdouni, R., & Chacón, J. (2007). Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Natural hazards, 41(1), 61-79.
  • Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33-41.
  • Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982-990. Magliulo, P., Di Lisio, A., Russo, F., & Zelano, A. (2008). Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Natural hazards, 47(3), 411-435.
  • MTA. (2002). 1:100 000 jeoloji haritası (Adapazarı H24-H25). Maden Tetkik ve Arama Genel Müdürlüğü. Ankara.
  • Nefeslioglu, H. A., Gokceoglu, C., & Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97(3-4), 171-191. https://doi.org/10.1016/j.enggeo.2008.01.004
  • Nefeslioglu, H. A., Sezer, E. A., Gokceoglu, C., & Ayas, Z. (2013). A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Computers & Geosciences, 59, 1-8. https://doi.org/10.1016/j.cageo. 2013.05.010
  • Okay, A. (2011). Tavşanlı Zonu: Anatolid-Torid Bloku’nun Dalma-Batmaya Uğramış Kuzey Ucu. Maden Tetkik ve Arama Dergisi (142), 195-226.
  • Pérez-Peña, J. V., Al-Awabdeh, M., Azañón, J. M., Galve, J. P., Booth- Rea, G., & Notti, D. (2017). SwathProfiler and NProfiler: Two new ArcGIS Add-ins for the automatic extraction of swath and normalized river profiles. Computers & Geosciences, 104, 135- 150.
  • Roslee, R., Mickey, A. C., Simon, N., & Norhisham, M. N. (2017). Landslide susceptibility analysis (LSA) using weighted overlay method (WOM) along the Genting Sempah to Bentong Highway, Pahang. Malaysian Journal of Geosciences (MJG), 1(2), 13-19.
  • Rotigliano, E., Cappadonia, C., Conoscenti, C., Costanzo, D., & Agnesi, V. (2012). Slope units-based flow susceptibility model: using validation tests to select controlling factors. Natural hazards, 61(1), 143-153.
  • Saaty, T. (1980). Analytical Hierarchy Process McGraw Hill Company. New York. p. 287.
  • Shit, P. K., Bhunia, G. S., & Maiti, R. (2016). Potential landslide susceptibility mapping using weighted overlay model (WOM). Modeling Earth Systems and Environment, 2(1), 21.
  • Suzen, M. L., & Doyuran, V. (2004). Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology, 71(3-4), 303-321.
  • Tribe, A. (1991). Automated recognition of valley heads from digital elevation models. Earth Surface Processes and Landforms, 16(1), 33-49. https://doi.org/10.1002/esp.3290160105
  • Varnes, D. J. (1978). Slope movement types and processes. Special report, 176, 11-33.
  • Yaralıoğlu, K. (2004). Decision support techniques in application. İlkem Ofset.
  • Yılmaz, H., & Özel, S. (2008). Crustal structure of the eastern part of Central Anatolia (Turkey). Turkish Journal of Earth Sciences, 17(1), 169-185.
  • Yilmaz, I. (2010). The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environmental Earth Sciences, 60(3), 505-519.
  • Zhang, H., Song, Y., Xu, S., He, Y., Li, Z., Yu, X., Liang, Y., Wu, W., & Wang, Y. (2022). Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China. Computers & Geosciences, 158, 104966.

Combination of slide-type landslides in different susceptibility: A case study of the Sakarya Basin Upstream

Year 2022, Issue: 80, 21 - 38, 30.06.2022

Mehmet Emin Cihangir

https://doi.org/10.17211/tcd.1065523
Cited By: 1

Abstract

Landslide susceptibility maps are important for the spatial prediction of landslides. Therefore,
the accuracy of landslide susceptibility models is the basis for hazard and risk studies.
The creation of a single model for all types of landslides in a region affects the success of
susceptibility. Since each type of landslide occurs in different mechanisms and materials, the
landslide controlling preparing conditions change. Creating susceptibility models according to
a single landslide type gives better results. For this reason, it is the aim of the study to reveal
how the susceptibility maps give results in qualitative and semi-quantitative approaches
according to the type of a single landslide main mechanism which is debris and soil slide.
For this purpose, susceptibility models were created for debris and soil type landslides
using Frequency Ratio, Analytical Hierarchy Process, Weighted Overlay, Modified Analytical
Hierarchy Process and GIS Matrix Models according to Varnes (1978) classification in the study
area located at the upstream of the Sakarya basin. While creating susceptibility depending on
the model results, the study area was divided into slope units, since a landslide would affect
the entire slope. According to the five model results, the results under the ROC change vary
between 0.79 and 0.92. This shows that the models give very good results and that the study
area is well represented in terms of landslide susceptibility. According to the results, the areas
where the landslide may be the highest and the lowest correspond to the common areas in all
models. As a result of the study, it was revealed that the models created according to the main
landslide type gave high results. These results made it easy to combine all models in a single
model. Thus, the study, which obtains a single model output from all models, contributes to
the improvement of landslide hazard and risk studies.

Keywords

Landslide Susceptibility Model Slope Unite Sakarya

References

  • Basharat, M., Shah, H. R., & Hameed, N. (2016). Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arabian Journal of Geosciences, 9(4), 1-19.
  • Brabb, E. E., & Pampeyan, E. H. (1972). Preliminary map of landslide deposits in San Mateo County, California (No. 344). US Geological Survey.
  • Carrara, A., Carratelli E.P., Merenda L. (1977) Computer-based data bank and statistical analysis of slope instability phenomena. Zeitschrift für Geomorphologie, 21 (1977), pp. 187-222.
  • Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16(5), 427-445. https://doi.org/10.1002/esp.3290160505
  • Carrara A., Cardinali M., Guzzetti F., Reichenbach P. (1995) GIS technology in mapping landslide hazard. A. Carrara, F. Guzzetti (Eds.), Geographical Information Systems in Assessing Natural Hazards, Kluwer Academic Publisher, Dordrecht, The Netherlands , pp. 135-175.
  • Casagli, N., Catani, F., Puglisi, C., Delmonaco, G., Ermini, L., & Margottini, C. (2004). An inventory-based approach to landslide susceptibility assessment and its application to the Virginio River Basin, Italy. Environmental and Engineering Geoscience, 10(3), 203-216. https://doi.org/10.2113/10.3.203
  • Cihangir, M. E., & Görüm, T. (2016). Kelkit vadisinin aşağı çığırında gelişmiş heyelanların dağılım deseni ve oluşumlarını kontrol eden faktörler. Türk Coğrafya Dergisi, (66), 19-28. https://doi. org/10.17211/tcd.84731
  • Cihangir, M. E., Görüm, T., & Nefeslioğlu, H. A. (2018). Heyelan tetikleyici faktörlerine bağlı mekânsal hassasiyet değerlendirmesi. Türk Cografya Dergisi (70), 133-142. https://doi.org/10.17211/ tcd.410998
  • Clerici, A., Perego, S., Tellini, C., & Vescovi, P. (2006). A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganza valley case study (Italian Northern Apennines). Environmental Geology, 50(7), 941-961.
  • Dagdelenler, G., Nefeslioglu, H. A., & Gokceoglu, C. (2016). Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey). Bulletin of Engineering Geology and the Environment, 75(2), 575-590.
  • DeGraff, J. V., & Romesburg, H. C. (2020). Regional landslide susceptibility assessment for wildland management: a matrix approach. Routledge. Coates D. , Vitek J. (Eds.), Thresholds in Geomorphology, George Allen and Unwin, London (1980), pp. 401-414.
  • Du, J., Glade, T., Woldai, T., Chai, B., & Zeng, B. (2020). Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas. Engineering Geology, 270, p.105572. https://doi.org/10.1016/j.enggeo. 2020.105572
  • Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M., & Poesen, J. (2009). Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Natural Hazards and Earth System Sciences, 9(2), 507-521.
  • Erener, A., & Düzgün, H. S. B. (2010). Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7(1), 55-68. https://doi.org/10.1007/s10346-009-0188-x
  • Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., & Savage, W. Z. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology, 102(3- 4), 99-111.
  • Fernández, C. I., Del Castillo, T. F., Hamdouni, R. E., & Montero, J. C. (1999). Verification of landslide susceptibility mapping: a case study. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group, 24(6), 537- 544. https://doi.org/10.1002/(SICI)1096-9837(199906)24:6%- 3C537::AID-ESP965%3E3.0.CO;2-6
  • Gorum, T., Gonencgil, B., Gokceoglu, C., & Nefeslioglu, H. (2008). Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: The Melen Gorge (NW Turkey). Natural hazards, 46(3), 323-351.
  • Guzzetti, F. (2021). Invited perspectives: Landslide populations–can they be predicted?. Natural Hazards and Earth System Sciences, 21(5), 1467-1471.
  • Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1-4), 181-216.
  • Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1-2), 166-184. https://doi.org/10.1016/j. geomorph.2006.04.007
  • Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72(1), 272-299. https://doi.org/ 10.1016/j.geomorph.2005.06.002
  • Hansen, A., (1984). Landslide hazard analysis. In: Brunsden, D., Prior, D.B. (Eds.), Slope Instability. Wiley and sons, New York, pp. 523–602.
  • Irigaray, C., Fernández, T., El Hamdouni, R., & Chacón, J. (2007). Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Natural hazards, 41(1), 61-79.
  • Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33-41.
  • Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982-990. Magliulo, P., Di Lisio, A., Russo, F., & Zelano, A. (2008). Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Natural hazards, 47(3), 411-435.
  • MTA. (2002). 1:100 000 jeoloji haritası (Adapazarı H24-H25). Maden Tetkik ve Arama Genel Müdürlüğü. Ankara.
  • Nefeslioglu, H. A., Gokceoglu, C., & Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97(3-4), 171-191. https://doi.org/10.1016/j.enggeo.2008.01.004
  • Nefeslioglu, H. A., Sezer, E. A., Gokceoglu, C., & Ayas, Z. (2013). A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Computers & Geosciences, 59, 1-8. https://doi.org/10.1016/j.cageo. 2013.05.010
  • Okay, A. (2011). Tavşanlı Zonu: Anatolid-Torid Bloku’nun Dalma-Batmaya Uğramış Kuzey Ucu. Maden Tetkik ve Arama Dergisi (142), 195-226.
  • Pérez-Peña, J. V., Al-Awabdeh, M., Azañón, J. M., Galve, J. P., Booth- Rea, G., & Notti, D. (2017). SwathProfiler and NProfiler: Two new ArcGIS Add-ins for the automatic extraction of swath and normalized river profiles. Computers & Geosciences, 104, 135- 150.
  • Roslee, R., Mickey, A. C., Simon, N., & Norhisham, M. N. (2017). Landslide susceptibility analysis (LSA) using weighted overlay method (WOM) along the Genting Sempah to Bentong Highway, Pahang. Malaysian Journal of Geosciences (MJG), 1(2), 13-19.
  • Rotigliano, E., Cappadonia, C., Conoscenti, C., Costanzo, D., & Agnesi, V. (2012). Slope units-based flow susceptibility model: using validation tests to select controlling factors. Natural hazards, 61(1), 143-153.
  • Saaty, T. (1980). Analytical Hierarchy Process McGraw Hill Company. New York. p. 287.
  • Shit, P. K., Bhunia, G. S., & Maiti, R. (2016). Potential landslide susceptibility mapping using weighted overlay model (WOM). Modeling Earth Systems and Environment, 2(1), 21.
  • Suzen, M. L., & Doyuran, V. (2004). Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey. Engineering Geology, 71(3-4), 303-321.
  • Tribe, A. (1991). Automated recognition of valley heads from digital elevation models. Earth Surface Processes and Landforms, 16(1), 33-49. https://doi.org/10.1002/esp.3290160105
  • Varnes, D. J. (1978). Slope movement types and processes. Special report, 176, 11-33.
  • Yaralıoğlu, K. (2004). Decision support techniques in application. İlkem Ofset.
  • Yılmaz, H., & Özel, S. (2008). Crustal structure of the eastern part of Central Anatolia (Turkey). Turkish Journal of Earth Sciences, 17(1), 169-185.
  • Yilmaz, I. (2010). The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environmental Earth Sciences, 60(3), 505-519.
  • Zhang, H., Song, Y., Xu, S., He, Y., Li, Z., Yu, X., Liang, Y., Wu, W., & Wang, Y. (2022). Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China. Computers & Geosciences, 158, 104966.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Human Geography
Journal Section Research Articles
Authors

Mehmet Emin Cihangir 0000-0001-8881-5308

Publication Date June 30, 2022
Acceptance Date March 23, 2022
Published in Issue Year 2022 Issue: 80

Cite

APA Cihangir, M. E. (2022). Kayma tipi heyelanların farklı duyarlılık modellerinde kombinasyonu: Sakarya Havzası Yukarı Çığırı örneği. Türk Coğrafya Dergisi(80), 21-38. https://doi.org/10.17211/tcd.1065523

Cited By

Characterization of an active landslide structure with integrated electrical resistivity tomography and multi‐channel analysis of surface waves methods in Değirmendere district, Sakarya (Türkiye)
Earth Surface Processes and Landforms
https://doi.org/10.1002/esp.5689
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Publisher: Turkish Geographical Society