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3D ALOS PALSAR görüntüleri ve sinir ağları ile geliştirilmiş heyelan duyarlılığı tahmini: Veri verimli bir çerçeve

Year 2024, Issue: 51, 115 - 126, 25.01.2024
https://doi.org/10.32003/igge.1380504

Abstract

Üç boyutlu verilere dayalı heyelan duyarlılık haritalaması LSM, insan güvenliği ve altyapının korunması açısından derin etkileri olan, büyüyen bir çalışma alanıdır. Mevcut yöntemlerin çoğu, tahmin doğruluğunu artırmak için kapsamlı girdi verilerine dayanır. Bu makale, LSM için uzaktan algılama veri gereksinimi çerçevesini tanıtmayı amaçlamaktadır. Yaklaşımımız yalnızca üç temel aşamadan oluşan tek bir 3D ALOS PALSAR görüntüsünden yararlanıyor: (A) veri ön işleme, (B) açıklayıcı faktörlerin türetilmesi ve (C) sinir ağı modelleme. Başlamak için ALOS PALSAR görüntüsünden 22 giriş değişkenini çıkardık. Bu değişkenler, İleri Beslemeli Sinir Ağı (FFNN) tahmincisinin geliştirilmesinde çok önemli bir rol oynadı. Tahmin yapısı giriş katmanında 22 değişken, gizli katmanda 150 nöron ve tek çıkış katmanından oluşmaktadır. Modelimiz 5.829 örnek nokta kullanılarak eğitilmiş ve ardından 745.810 noktalı heyelan duyarlılık (LS) haritasının oluşturulmasında kullanılmıştır. Genel doğruluk metriğine dayalı olarak model, eğitim sırasında %89,3 ve test sırasında %82,3'e ulaşarak etkileyici bir performans doğruluğu sergiledi. Ayrıca Alıcı Çalışma Karakteristiği eğrisine göre eğitim sırasında %95,22 ve test sırasında %84,7 gibi güçlü bir performans gösterdi. Sonuçlar, Karabük'teki çalışma alanının 3,53 km²'sinin (%3,03) çok yüksek duyarlılık kategorisine girdiğini göstermiştir. Sonuç olarak, önerdiğimiz çerçevenin uygulanması, uzaktan algılama-veri gereksinimi tahminlerini dikkate değer bir doğrulukla geliştirme yeteneğinin altını çiziyor. Bu çerçeve, zorlu topografik koşullara ve kapsamlı veri kapsamına sahip bölgelerdeki gelecekteki gelişmeler için değerli bilgiler sunmaktadır. Ayrıca, veri kalitesi tutarsızlıklarını ve veri güncelleme sorunlarını etkili bir şekilde ele alır.

References

  • Adel, K., Katlane, R., Haddad, R., & Rabia, M. C. (2023). Landslide susceptibility mapping by Frequency Ratio and Fuzzy logic approach: A case study of Mogods and Hedil (Northern Tunisia).
  • Arca, D., Keskin Citiroglu, H., & Tasoglu, I. K. (2019). A comparison of GIS-based landslide susceptibility assessment of the Satuk village (Yenice, NW Turkey) by frequency ratio and multi-criteria decision methods. Environmental Earth Sciences, 78(81), 4–13. https://doi.org/10.1007/s12665-019-8094-6
  • Çan, T., Duman, T. Y., Olgun, Ş., Çörekçioğlu, Ş., Karakaya-Gülmez, F., Elmacı, H., Hamzaçebi, S., & Emre, Ö. (2013). Türkiye heyelan veri tabanı. TMMOB Coğrafi Bilgi Sistemleri Kongresi.
  • Chae, B.-G., Park, H.-J., Catani, F., Simoni, A., & Berti, M. (2017). Landslide prediction, monitoring and early warning: A concise review of state-of-the-art. Geosciences Journal, 21(6), 1033–1070. https://doi.org/10.1007/s12303-017-0034-4
  • Chaudhary, P., Chhetri, S. K., Joshi, K. M., Shrestha, B. M., & Kayastha, P. (2015). Application of an Analytic Hierarchy Process (AHP) in the GIS interface for suitable fire site selection: A case study from Kathmandu Metropolitan City, Nepal. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2015.10.001
  • Duman, T. Y., Emre, Ö., Çan, T., Nefeslioğlu, H. A., Keçer, M., Doğan, A., Durmaz, S., & Ateş, Ş. (2005). Türkiye heyelan envanteri haritası-1: 500.000 ölçekli Zonguldak Paftası. MTA Özel Yayınlar Serisi-4, Ankara.
  • Fu, Z., Wang, F., Dou, J., Nam, K., & Ma, H. (2023). Enhanced absence sampling technique for data-driven landslide susceptibility mapping: A case study in Songyang County, China. Remote Sensing, 15(13). https://doi.org/10.3390/rs15133345
  • Haque, U., Blum, P., da Silva, P. F., Andersen, P., Pilz, J., Chalov, S. R., Malet, J.-P., Auflič, M. J., Andres, N., Poyiadji, E., Lamas, P. C., Zhang, W., Peshevski, I., Pétursson, H. G., Kurt, T., Dobrev, N., García-Davalillo, J. C., Halkia, M., Ferri, S., … Keellings, D. (2016). Fatal landslides in Europe. Landslides, 13(6), 1545–1554. https://doi.org/10.1007/s10346-016-0689-3
  • Lee, D.-H., Kim, Y.-T., & Lee, S.-R. (2020). Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071194
  • Liang, D., Tsai, C.-F., & Wu, H.-T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289–297. https://doi.org/10.1016/j.knosys.2014.10.010
  • Liang, Z., Peng, W., Liu, W., Huang, H., Huang, J., Lou, K., Liu, G., & Jiang, K. (2023). Exploration and comparison of the effect of conventional and advanced modeling algorithms on landslide susceptibility prediction: A case study from Yadong Country, Tibet. Applied Sciences, 13(12). https://doi.org/10.3390/app13127276
  • Nefeslioglu, H. A., San, B. T., Gokceoglu, C., & Duman, T. Y. (2012). An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping. International Journal of Applied Earth Observation and Geoinformation, 14(1), 40–60. https://doi.org/10.1016/j.jag.2011.08.005
  • Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747–759. http://dx.doi.org/10.1016/j.envsoft.2009.10.016
  • Sameen, M. I., Pradhan, B., & Lee, S. (2020). Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena, 186 (September), 104249. https://doi.org/10.1016/j.catena.2019.104249
  • Song, K.-Y., Oh, H.-J., Choi, J., Park, I., Lee, C., & Lee, S. (2012). Prediction of landslides using ASTER imagery and data mining models. Advances in Space Research, 49(5), 978–993. https://doi.org/10.1016/j.asr.2011.11.035
  • Yilmaz, I. (2009a). A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bulletin of Engineering Geology and the Environment, 68(3), 297–306. https://doi.org/10.1007/s10064-009-0185-2
  • Yilmaz, I. (2009b). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers and Geosciences, 35(6), 1125–1138. https://doi.org/10.1016/j.cageo.2008.08.007
  • Zhu, L., Liu, L., & Yu, C. (2023). Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model. arXiv Preprint arXiv:2304.06054.

Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework

Year 2024, Issue: 51, 115 - 126, 25.01.2024
https://doi.org/10.32003/igge.1380504

Abstract

Landslide susceptibility mapping (LSM) founded on DEM is a growing research field with profound implications for human safety and infrastructure preservation. Many existing methods rely on extensive input data to enhance predictive accuracy. This paper aims to introduce a remote sensing-data-requirement framework for LSM. Our approach exclusively leverages a single ALOS PALSAR image, comprising three key steps: (1) Pre-processing, (2) derivation of explanatory variables, and (3) neural network modeling. To begin, we extracted 22 input variables from the ALOS PALSAR image. These variables played a pivotal role in developing the Neural Network (NN) predictor. The predictor structure consists of 22 variables in the input layer, 150 neurons in the hidden layer, and a single output layer. Our model was trained using 5,829 sample points, and subsequently, it was employed to generate landslide susceptibility (LS) map with 745,810 points. Based on the Overall accuracy metric, the model exhibited impressive performance accuracy, achieving 89.3% training and 82.3% testing accuracies. Additionally, it demonstrated a strong performance of 95.22% during training and 84.7% during testing according to the ROC curve. In conclusion, the implementation of our proposed method underscores its ability to develop remarkable accuracy model with remote sensing-data-requirement. This framework offers valuable insights for future progress in regions with challenging conditions and extensive data coverage. Moreover, it effectively handles data quality inconsistencies and data updating issues.

References

  • Adel, K., Katlane, R., Haddad, R., & Rabia, M. C. (2023). Landslide susceptibility mapping by Frequency Ratio and Fuzzy logic approach: A case study of Mogods and Hedil (Northern Tunisia).
  • Arca, D., Keskin Citiroglu, H., & Tasoglu, I. K. (2019). A comparison of GIS-based landslide susceptibility assessment of the Satuk village (Yenice, NW Turkey) by frequency ratio and multi-criteria decision methods. Environmental Earth Sciences, 78(81), 4–13. https://doi.org/10.1007/s12665-019-8094-6
  • Çan, T., Duman, T. Y., Olgun, Ş., Çörekçioğlu, Ş., Karakaya-Gülmez, F., Elmacı, H., Hamzaçebi, S., & Emre, Ö. (2013). Türkiye heyelan veri tabanı. TMMOB Coğrafi Bilgi Sistemleri Kongresi.
  • Chae, B.-G., Park, H.-J., Catani, F., Simoni, A., & Berti, M. (2017). Landslide prediction, monitoring and early warning: A concise review of state-of-the-art. Geosciences Journal, 21(6), 1033–1070. https://doi.org/10.1007/s12303-017-0034-4
  • Chaudhary, P., Chhetri, S. K., Joshi, K. M., Shrestha, B. M., & Kayastha, P. (2015). Application of an Analytic Hierarchy Process (AHP) in the GIS interface for suitable fire site selection: A case study from Kathmandu Metropolitan City, Nepal. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2015.10.001
  • Duman, T. Y., Emre, Ö., Çan, T., Nefeslioğlu, H. A., Keçer, M., Doğan, A., Durmaz, S., & Ateş, Ş. (2005). Türkiye heyelan envanteri haritası-1: 500.000 ölçekli Zonguldak Paftası. MTA Özel Yayınlar Serisi-4, Ankara.
  • Fu, Z., Wang, F., Dou, J., Nam, K., & Ma, H. (2023). Enhanced absence sampling technique for data-driven landslide susceptibility mapping: A case study in Songyang County, China. Remote Sensing, 15(13). https://doi.org/10.3390/rs15133345
  • Haque, U., Blum, P., da Silva, P. F., Andersen, P., Pilz, J., Chalov, S. R., Malet, J.-P., Auflič, M. J., Andres, N., Poyiadji, E., Lamas, P. C., Zhang, W., Peshevski, I., Pétursson, H. G., Kurt, T., Dobrev, N., García-Davalillo, J. C., Halkia, M., Ferri, S., … Keellings, D. (2016). Fatal landslides in Europe. Landslides, 13(6), 1545–1554. https://doi.org/10.1007/s10346-016-0689-3
  • Lee, D.-H., Kim, Y.-T., & Lee, S.-R. (2020). Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071194
  • Liang, D., Tsai, C.-F., & Wu, H.-T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289–297. https://doi.org/10.1016/j.knosys.2014.10.010
  • Liang, Z., Peng, W., Liu, W., Huang, H., Huang, J., Lou, K., Liu, G., & Jiang, K. (2023). Exploration and comparison of the effect of conventional and advanced modeling algorithms on landslide susceptibility prediction: A case study from Yadong Country, Tibet. Applied Sciences, 13(12). https://doi.org/10.3390/app13127276
  • Nefeslioglu, H. A., San, B. T., Gokceoglu, C., & Duman, T. Y. (2012). An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping. International Journal of Applied Earth Observation and Geoinformation, 14(1), 40–60. https://doi.org/10.1016/j.jag.2011.08.005
  • Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747–759. http://dx.doi.org/10.1016/j.envsoft.2009.10.016
  • Sameen, M. I., Pradhan, B., & Lee, S. (2020). Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena, 186 (September), 104249. https://doi.org/10.1016/j.catena.2019.104249
  • Song, K.-Y., Oh, H.-J., Choi, J., Park, I., Lee, C., & Lee, S. (2012). Prediction of landslides using ASTER imagery and data mining models. Advances in Space Research, 49(5), 978–993. https://doi.org/10.1016/j.asr.2011.11.035
  • Yilmaz, I. (2009a). A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bulletin of Engineering Geology and the Environment, 68(3), 297–306. https://doi.org/10.1007/s10064-009-0185-2
  • Yilmaz, I. (2009b). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers and Geosciences, 35(6), 1125–1138. https://doi.org/10.1016/j.cageo.2008.08.007
  • Zhu, L., Liu, L., & Yu, C. (2023). Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model. arXiv Preprint arXiv:2304.06054.
There are 18 citations in total.

Details

Primary Language English
Subjects Geographic Information Systems
Journal Section RESEARCH ARTICLE
Authors

Sohaib K M Abujayyab 0000-0002-6692-3567

Publication Date January 25, 2024
Submission Date October 24, 2023
Acceptance Date December 3, 2023
Published in Issue Year 2024 Issue: 51

Cite

APA Abujayyab, S. K. M. (2024). Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework. Lnternational Journal of Geography and Geography Education(51), 115-126. https://doi.org/10.32003/igge.1380504
AMA Abujayyab SKM. Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework. IGGE. January 2024;(51):115-126. doi:10.32003/igge.1380504
Chicago Abujayyab, Sohaib K M. “Enhanced Landslide Susceptibility Prediction With 3D ALOS PALSAR Imagery and Neural Networks: A Data-Efficient Framework”. Lnternational Journal of Geography and Geography Education, no. 51 (January 2024): 115-26. https://doi.org/10.32003/igge.1380504.
EndNote Abujayyab SKM (January 1, 2024) Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework. lnternational Journal of Geography and Geography Education 51 115–126.
IEEE S. K. M. Abujayyab, “Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework”, IGGE, no. 51, pp. 115–126, January 2024, doi: 10.32003/igge.1380504.
ISNAD Abujayyab, Sohaib K M. “Enhanced Landslide Susceptibility Prediction With 3D ALOS PALSAR Imagery and Neural Networks: A Data-Efficient Framework”. lnternational Journal of Geography and Geography Education 51 (January 2024), 115-126. https://doi.org/10.32003/igge.1380504.
JAMA Abujayyab SKM. Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework. IGGE. 2024;:115–126.
MLA Abujayyab, Sohaib K M. “Enhanced Landslide Susceptibility Prediction With 3D ALOS PALSAR Imagery and Neural Networks: A Data-Efficient Framework”. Lnternational Journal of Geography and Geography Education, no. 51, 2024, pp. 115-26, doi:10.32003/igge.1380504.
Vancouver Abujayyab SKM. Enhanced landslide susceptibility prediction with 3D ALOS PALSAR imagery and neural networks: A data-efficient framework. IGGE. 2024(51):115-26.