Araştırma Makalesi
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The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018)

Yıl 2021, Cilt: 8 Sayı: 1, 57 - 71, 01.05.2021
https://doi.org/10.9733/JGG.2021R0005.E

Öz

This study aimed to investigate the temporal change in land-use/cover in the Beyşehir-Kaşaklı Subbasin, which is one of the nine subbasins of the Konya Closed Basin and known as the largest closed basin in Turkey, using Remote Sensing and Geographic Information Systems techniques. For this purpose, in the study, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager digital satellite images obtained in the years 1984, 1990, 1996, 2000, 2006, 2012, and 2018 were used. The Support Vector Machines (SVM) method was applied as the classification method. In order to apply the SVM method, firstly, the kernel function and parameter set, giving the highest accuracy in the classification, were selected. In the study, four different kernel functions and different parameter sets were experienced as different from each other. Seventy-two different models in total were applied using different combinations of parameters. As a result of the trials of seventy-two different parameters, it was concluded that the method and algorithm giving the most accurate result with 83.81% classification accuracy and 0.7949 Kappa statistics were the polynomial function of SVMs. As a result of the classification process examined the period between 1984 and 2018 using the determined algorithm and parameters, it was detected that artificial surfaces increased by 418%, arable agricultural lands and pastures decreased by 14%, forests and semi-natural areas increased by 4%, and coastal wetlands on the coasts increased by 6%. On the other hand, the surface area of the water bodies in the region, which demonstrated a decreasing trend until the year 2003, was determined to increase by 3% with the establishment of Suğla Storage in 2003.

Destekleyen Kurum

Necmettin Erbakan Üniversitesi Bilimsel Araştırma Projeleri Birimi

Proje Numarası

191419002

Teşekkür

This study is derived from the ongoing thesis titled "Investigation of Sustainable Land Management in Beyşehir-Kaşaklı Sub-Basin Using Geographic Information Systems and Remote Sensing Techniques".This work was supported by Necmettin Erbakan University Scientific Research Projects Unit with the Project Code 191419002.

Kaynakça

  • Acheampong, M., Yu, Q., Enomah, L. D., Anchang, J., & Eduful, M. (2018). Land use/cover change in Ghana’s oil city: Assessing the impact of neoliberal economic policies and implications for sustainable development goal number one–A remote sensing and GIS approach. Land Use Policy, 73, 373-384.
  • Aslan, A. (2012). Hazine arazilerindeki işgallerin belirlenmesinde ve satışa esas hazine arazilerinin kıymetlendirilmesinde bilgi teknolojilerinin kullanımı (Master Thesis), Selçuk University, The Graduate School of Natural and Applied Science, Konya, Turkey (in Turkish).
  • Atlas 2019 application (2020). Ministry of Environment and Urbanization. Directorate General of Geographic Information Systems. https://basic.atlas.gov.tr/?_appToken=&metadataId= (Accessed: 4 February 2020).
  • Ayhan, S., & Erdogmus, S. (2014). Destek vektör makineleriyle sınıflandırma problemlerinin çözümü için çekirdek fonksiyonu seçimi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 9(1), 175-201.
  • Banerjee, R., & Srivastava, P. K. (2013). Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing. Land Use Policy, 34, 193-203.
  • Campbell, J.B. (1996). Introduction to Remote Sensing. New York: Guilford Press.
  • Chen, S., Li, S., Ma, W., Ji, W., Xu, D., Shi, Z., & Zhang, G. (2019). Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification. European Journal of Soil Science, 70(1), 42-53.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • CORINE Project (2020). Ministry of Agriculture and Forestry. https://corine.tarimorman.gov.tr/corineportal/ (Accessed: 13 January 2020).
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • Dengiz, O., & Turan, I. D. (2014). Uzaktan Algılama ve Coğrafi Bilgi Sistem Teknikleri Kullanılarak Arazi Örtüsü/Arazi Kullanımı Zamansal Değişimin Belirlenmesi: Samsun Merkez İlçesi Örneği (1984-2011). Türkiye Tarımsal Araştırmalar Dergisi, 1(1), 78-90.
  • Dewan, A. M., & Yamaguchi, Y. (2009). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environmental monitoring and assessment, 150(1-4), 237.
  • Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29(4), 1185-1206.
  • Dodiya, D., Goswami, S., Chauhan, D., Bhuva, M., & Parekh, R. (2019). Land use & land cover change detection using GIS & Remote Sensing. International Research Journal of Engineering and Technology (IRJET), 6(4), 3001-3005.
  • Donmez, S. O. (2015). Obje tabanlı sınıflandırma yaklaşımı ile 3. seviye ulusal arazi örtüsü/kullanımının belirlenmesi (Master Thesis), Istanbul Technical University, The Graduate School of Natural and Applied Science, Istanbul, Turkey (in Turkish).
  • European Topic Centre/Land Cover (ETC/LC). CORINE land cover. (1995), http://www.eea.europa.eu/publications/COR0-landcover (Accessed: 24 December 2019).
  • Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on geoscience and remote sensing, 42(6), 1335-1343.
  • Geymen, A., & Baz, I. (2008). Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area. Environmental monitoring and assessment, 136(1-3), 449-459.
  • Gulersoy, A. E. (2008). Bakırçay Havzası'nda doğal ortam koşulları ile arazi kullanımı arasındaki ilişkiler (Doctoral Dissertation), Dokuz Eylül University, Institute of Educational Sciences, Izmir, Turkey (in Turkish).
  • Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725-749.
  • Kara, F., & Karatepe, A. (2012). Uzaktan Algılama teknolojileri ile Beykoz ilçesi (1986-2011) Arazi Kullanımı Değişim Analizi. Marmara Coğrafya Dergisi, (25), 378-389.
  • Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
  • Koylu, Ü., & Geymen, A. (2016). GIS and remote sensing techniques for the assessment of the impact of land use change on runoff. Arabian Journal of Geosciences, 9(7), 484.
  • Kumar, S., Radhakrishnan, N., & Mathew, S. (2019). Land Use Land Cover Change Detection and Forecasting for Tiruchirappalli City Using Remote Sensing and GIS. Journal of Remote Sensing & GIS, 3(1-3), 96-107.
  • Li, S., Li, H., Li, M., Shyr, Y., Xie, L., & Li, Y. (2009). Improved prediction of lysine acetylation by support vector machines. Protein and peptide letters, 16(8), 977-983.
  • Mansour, S., Al-Belushi, M., & Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91, 104414.
  • Orhan, O. (2014). Konya Kapalı Havzası'nda Uzaktan Algılama ve CBS teknolojileri ile iklim değişikliği ve kuraklık analizi (Master Thesis), Aksaray University, Graduate School of Natural and Applied Science, Aksaray, Turkey (in Turkish).
  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
  • Punia, M., Joshi, P. K., & Porwal, M. C. (2011). Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data. Expert systems with Applications, 38(5), 5577-5583.
  • Rozenstein, O., & Karnieli, A. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography, 31(2), 533-544.
  • Sari, H., & Ozsahin, E. (2016). Spatiotemporal change in the LULC (Landuse/Landcover) characteristics of Tekirdag Province based on the CORINE (Thrace, Turkey). Fresenius Environmental Bulletin, 25(11), 4694-4707.
  • Soman, K. P., Loganathan, R., & Ajay, V. (2009). Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd.
  • Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250-1265.
  • Sunar, F. (2017). Dijital Görüntü İşleme, Eskişehir, Turkey: T.C. Anadolu Üniversitesi Yayını.
  • Topaloglu, R. H., Sertel, E., & Musaoglu, N. (2016). Assessment of classification accuracies of sentinel-2 and landsat-8 data for land cover/use mapping. International archives of the photogrammetry, remote sensing & spatial Information Sciences, 41.
  • United States Geological Research Institute (USGS). (2020). https://Earthexplorer.Usgs.Gov/ (Accessed: 01 January 2020).
  • Ustuner, M., Sanli, F. B., & Dixon, B. (2015). Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis. European Journal of Remote Sensing, 48(1), 403-422.
  • Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.
  • Wentz, E. A., Nelson, D., Rahman, A., Stefanov, W. L., & Roy, S. S. (2008). Expert system classification of urban land use/cover for Delhi, India. International Journal of Remote Sensing, 29(15), 4405-4427.
  • Yakar, A. (2013). Kentsel Gelişme Alanlarında Arazi Kullanımı Ve Değişiminin Sürdürülebilir Arazi Yönetimi Açısından İncelenmesi: Trabzon İli Örneği (Master Thesis), Karadeniz Technical University, The Graduate School of Natural and Applied Science, Trabzon, Turkey (in Turkish).
  • Yu, L., Lan, J., Zeng, Y., & Zou, J. (2019). Comparison of Land Cover Types Classification Methods Using Tiangong-2 Multispectral Image. In Proceedings of the Tiangong-2 Remote Sensing Application Conference, 241-253.
  • Zhang, R., & Zhu, D. (2011). Study of land cover classification based on knowledge rules using high-resolution remote sensing images. Expert Systems with Applications, 38(4), 3647-3652.

Türkiye'deki Beyşehir havzasında arazi kullanım/örtüsündeki zamansal değişimin incelenmesi için destek vektör makine sınıflandırıcılarının performans analizleri (1984-2018)

Yıl 2021, Cilt: 8 Sayı: 1, 57 - 71, 01.05.2021
https://doi.org/10.9733/JGG.2021R0005.E

Öz

Bu çalışmada, Türkiye’nin en büyük kapalı havzası olarak bilinen Konya Kapalı Havzası’nın dokuz alt havzasından biri olan Beyşehir-Kaşaklı Alt Havzası’nda meydana gelen arazi kullanımındaki/örtüsündeki zamansal değişikliklerin Uzaktan Algılama teknikleriyle incelenmesi amaçlanmıştır. Bu amaçla çalışmada 1984, 1990, 1996, 2000, 2006, 2012 ve 2018 yıllarında elde edilen Landsat Thematic Mapper, Enhanced Thematic Mapper ve Operational Land Imager dijital uydu görüntüleri kullanılmıştır. Çalışmada piksel tabanlı sınıflandırmalar arasından Destek Vektör Makineleri (DVM) yöntemi uygulanmıştır. DVM yönteminin uygulanması için öncelikle sınıflandırmada en yüksek doğruluğu veren kernel fonksiyon ve parametre seti seçimi yapılmıştır. Çalışmada birbirinden farklı olarak 4 farklı kernel fonksiyon ve farklı parametre setleri denenmiştir. Farklı parametre kombinasyonları kullanılarak toplamda 72 farklı model uygulanmıştır. Belirlenen modeller ile algoritma, kernel fonksiyon ve bu kernele ait parametre seçiminin sınıflandırma doğruluğuna etkisi irdelenmiştir. 72 ayrı parametrenin denemesi sonucunda %83.81 sınıflandırma doğruluğu, 0.7949 Kappa istatistiği ile en doğru sonucu veren yöntem ve algoritmanın DVM’ lere ait polinomal fonksiyon olduğu sonucuna varılmıştır. Belirlenen algoritma ve parametreler kullanılarak 1984 ve 2018 yılları arası irdelenen sınıflandırma işleminin sonucunda yapay yüzeylerin %418 arttığı, ekilebilir tarım alanlarının ve meraların %14 azaldığı, orman ve yarı doğal alanların %4 arttığı, kıyılarda bulunan kıyısal sulak alanların %6 oranında arttığı ve bölgedeki su yapısının ise 2003 yılına kadar azalan bir trend gösterirken 2003 yılında kurulan Suğla Depolaması ile birlikte su yapısının yüzey alanının %3 arttığı tespit edilmiştir.

Proje Numarası

191419002

Kaynakça

  • Acheampong, M., Yu, Q., Enomah, L. D., Anchang, J., & Eduful, M. (2018). Land use/cover change in Ghana’s oil city: Assessing the impact of neoliberal economic policies and implications for sustainable development goal number one–A remote sensing and GIS approach. Land Use Policy, 73, 373-384.
  • Aslan, A. (2012). Hazine arazilerindeki işgallerin belirlenmesinde ve satışa esas hazine arazilerinin kıymetlendirilmesinde bilgi teknolojilerinin kullanımı (Master Thesis), Selçuk University, The Graduate School of Natural and Applied Science, Konya, Turkey (in Turkish).
  • Atlas 2019 application (2020). Ministry of Environment and Urbanization. Directorate General of Geographic Information Systems. https://basic.atlas.gov.tr/?_appToken=&metadataId= (Accessed: 4 February 2020).
  • Ayhan, S., & Erdogmus, S. (2014). Destek vektör makineleriyle sınıflandırma problemlerinin çözümü için çekirdek fonksiyonu seçimi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 9(1), 175-201.
  • Banerjee, R., & Srivastava, P. K. (2013). Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing. Land Use Policy, 34, 193-203.
  • Campbell, J.B. (1996). Introduction to Remote Sensing. New York: Guilford Press.
  • Chen, S., Li, S., Ma, W., Ji, W., Xu, D., Shi, Z., & Zhang, G. (2019). Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification. European Journal of Soil Science, 70(1), 42-53.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • CORINE Project (2020). Ministry of Agriculture and Forestry. https://corine.tarimorman.gov.tr/corineportal/ (Accessed: 13 January 2020).
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • Dengiz, O., & Turan, I. D. (2014). Uzaktan Algılama ve Coğrafi Bilgi Sistem Teknikleri Kullanılarak Arazi Örtüsü/Arazi Kullanımı Zamansal Değişimin Belirlenmesi: Samsun Merkez İlçesi Örneği (1984-2011). Türkiye Tarımsal Araştırmalar Dergisi, 1(1), 78-90.
  • Dewan, A. M., & Yamaguchi, Y. (2009). Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environmental monitoring and assessment, 150(1-4), 237.
  • Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29(4), 1185-1206.
  • Dodiya, D., Goswami, S., Chauhan, D., Bhuva, M., & Parekh, R. (2019). Land use & land cover change detection using GIS & Remote Sensing. International Research Journal of Engineering and Technology (IRJET), 6(4), 3001-3005.
  • Donmez, S. O. (2015). Obje tabanlı sınıflandırma yaklaşımı ile 3. seviye ulusal arazi örtüsü/kullanımının belirlenmesi (Master Thesis), Istanbul Technical University, The Graduate School of Natural and Applied Science, Istanbul, Turkey (in Turkish).
  • European Topic Centre/Land Cover (ETC/LC). CORINE land cover. (1995), http://www.eea.europa.eu/publications/COR0-landcover (Accessed: 24 December 2019).
  • Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on geoscience and remote sensing, 42(6), 1335-1343.
  • Geymen, A., & Baz, I. (2008). Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area. Environmental monitoring and assessment, 136(1-3), 449-459.
  • Gulersoy, A. E. (2008). Bakırçay Havzası'nda doğal ortam koşulları ile arazi kullanımı arasındaki ilişkiler (Doctoral Dissertation), Dokuz Eylül University, Institute of Educational Sciences, Izmir, Turkey (in Turkish).
  • Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725-749.
  • Kara, F., & Karatepe, A. (2012). Uzaktan Algılama teknolojileri ile Beykoz ilçesi (1986-2011) Arazi Kullanımı Değişim Analizi. Marmara Coğrafya Dergisi, (25), 378-389.
  • Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
  • Koylu, Ü., & Geymen, A. (2016). GIS and remote sensing techniques for the assessment of the impact of land use change on runoff. Arabian Journal of Geosciences, 9(7), 484.
  • Kumar, S., Radhakrishnan, N., & Mathew, S. (2019). Land Use Land Cover Change Detection and Forecasting for Tiruchirappalli City Using Remote Sensing and GIS. Journal of Remote Sensing & GIS, 3(1-3), 96-107.
  • Li, S., Li, H., Li, M., Shyr, Y., Xie, L., & Li, Y. (2009). Improved prediction of lysine acetylation by support vector machines. Protein and peptide letters, 16(8), 977-983.
  • Mansour, S., Al-Belushi, M., & Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91, 104414.
  • Orhan, O. (2014). Konya Kapalı Havzası'nda Uzaktan Algılama ve CBS teknolojileri ile iklim değişikliği ve kuraklık analizi (Master Thesis), Aksaray University, Graduate School of Natural and Applied Science, Aksaray, Turkey (in Turkish).
  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
  • Punia, M., Joshi, P. K., & Porwal, M. C. (2011). Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data. Expert systems with Applications, 38(5), 5577-5583.
  • Rozenstein, O., & Karnieli, A. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography, 31(2), 533-544.
  • Sari, H., & Ozsahin, E. (2016). Spatiotemporal change in the LULC (Landuse/Landcover) characteristics of Tekirdag Province based on the CORINE (Thrace, Turkey). Fresenius Environmental Bulletin, 25(11), 4694-4707.
  • Soman, K. P., Loganathan, R., & Ajay, V. (2009). Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd.
  • Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250-1265.
  • Sunar, F. (2017). Dijital Görüntü İşleme, Eskişehir, Turkey: T.C. Anadolu Üniversitesi Yayını.
  • Topaloglu, R. H., Sertel, E., & Musaoglu, N. (2016). Assessment of classification accuracies of sentinel-2 and landsat-8 data for land cover/use mapping. International archives of the photogrammetry, remote sensing & spatial Information Sciences, 41.
  • United States Geological Research Institute (USGS). (2020). https://Earthexplorer.Usgs.Gov/ (Accessed: 01 January 2020).
  • Ustuner, M., Sanli, F. B., & Dixon, B. (2015). Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis. European Journal of Remote Sensing, 48(1), 403-422.
  • Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.
  • Wentz, E. A., Nelson, D., Rahman, A., Stefanov, W. L., & Roy, S. S. (2008). Expert system classification of urban land use/cover for Delhi, India. International Journal of Remote Sensing, 29(15), 4405-4427.
  • Yakar, A. (2013). Kentsel Gelişme Alanlarında Arazi Kullanımı Ve Değişiminin Sürdürülebilir Arazi Yönetimi Açısından İncelenmesi: Trabzon İli Örneği (Master Thesis), Karadeniz Technical University, The Graduate School of Natural and Applied Science, Trabzon, Turkey (in Turkish).
  • Yu, L., Lan, J., Zeng, Y., & Zou, J. (2019). Comparison of Land Cover Types Classification Methods Using Tiangong-2 Multispectral Image. In Proceedings of the Tiangong-2 Remote Sensing Application Conference, 241-253.
  • Zhang, R., & Zhu, D. (2011). Study of land cover classification based on knowledge rules using high-resolution remote sensing images. Expert Systems with Applications, 38(4), 3647-3652.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Münevver Gizem Gümüş 0000-0003-4606-2277

S. Savaş Durduran 0000-0003-0509-4037

Proje Numarası 191419002
Yayımlanma Tarihi 1 Mayıs 2021
Gönderilme Tarihi 13 Temmuz 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 8 Sayı: 1

Kaynak Göster

APA Gümüş, M. G., & Durduran, S. S. (2021). The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018). Jeodezi Ve Jeoinformasyon Dergisi, 8(1), 57-71. https://doi.org/10.9733/JGG.2021R0005.E
AMA Gümüş MG, Durduran SS. The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018). hkmojjd. Mayıs 2021;8(1):57-71. doi:10.9733/JGG.2021R0005.E
Chicago Gümüş, Münevver Gizem, ve S. Savaş Durduran. “The Performance Analyses of Support Vector Machine Classifiers for Examination of the Temporal Change of Land-use/Cover in the Beyşehir Basin in Turkey (1984-2018)”. Jeodezi Ve Jeoinformasyon Dergisi 8, sy. 1 (Mayıs 2021): 57-71. https://doi.org/10.9733/JGG.2021R0005.E.
EndNote Gümüş MG, Durduran SS (01 Mayıs 2021) The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018). Jeodezi ve Jeoinformasyon Dergisi 8 1 57–71.
IEEE M. G. Gümüş ve S. S. Durduran, “The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018)”, hkmojjd, c. 8, sy. 1, ss. 57–71, 2021, doi: 10.9733/JGG.2021R0005.E.
ISNAD Gümüş, Münevver Gizem - Durduran, S. Savaş. “The Performance Analyses of Support Vector Machine Classifiers for Examination of the Temporal Change of Land-use/Cover in the Beyşehir Basin in Turkey (1984-2018)”. Jeodezi ve Jeoinformasyon Dergisi 8/1 (Mayıs 2021), 57-71. https://doi.org/10.9733/JGG.2021R0005.E.
JAMA Gümüş MG, Durduran SS. The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018). hkmojjd. 2021;8:57–71.
MLA Gümüş, Münevver Gizem ve S. Savaş Durduran. “The Performance Analyses of Support Vector Machine Classifiers for Examination of the Temporal Change of Land-use/Cover in the Beyşehir Basin in Turkey (1984-2018)”. Jeodezi Ve Jeoinformasyon Dergisi, c. 8, sy. 1, 2021, ss. 57-71, doi:10.9733/JGG.2021R0005.E.
Vancouver Gümüş MG, Durduran SS. The performance analyses of support vector machine classifiers for examination of the temporal change of land-use/cover in the Beyşehir Basin in Turkey (1984-2018). hkmojjd. 2021;8(1):57-71.