Araştırma Makalesi
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Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Yıl 2019, , 782 - 787, 01.10.2019
https://doi.org/10.16984/saufenbilder.479801

Öz

Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set show that the proposed method achieves 83.07% classification accuracy and is superior over logistic regression, support vector machine and neural network based approaches and achieves similar results when compared to a decision trees-based model. 

Kaynakça

  • A. Morales-Esteban, F. Martínez-Álvarez, A. Troncoso, J. Justo and C. Rubio-Escudero, "Pattern recognition to forecast seismic time series", Expert Systems with Applications, vol. 37, no. 12, pp. 8333-8342, 2010.
  • K. Asim, F. Martínez-Álvarez, A. Basit and T. Iqbal, "Earthquake magnitude prediction in Hindukush region using machine learning techniques", Natural Hazards, vol. 85, no. 1, pp. 471-486, 2016.
  • H. Cloke and F. Pappenberger, "Ensemble flood forecasting: A review", Journal of Hydrology, vol. 375, no. 3-4, pp. 613-626, 2009.
  • B. Bhattacharya and D. Solomatine, "Neural networks and M5 model trees in modelling water level–discharge relationship", Neurocomputing, vol. 63, pp. 381-396, 2005.
  • H. Langer, S. Falsaperla, A. Messina and S. Spampinato, "Perfomance of a new multistation alarm system for volcanic activity based on neural network techniques", in Second European Conference on Earthquake Engineering and Seismology, 2014.
  • J. Parra, O. Fuentes, E. Anthony and V. Kreinovich, "Use of Machine Learning to Analyze and – Hopefully – Predict Volcano Activity", Acta Polytechnica Hungarica, vol. 14, no. 3, 2017.
  • UNISDR: Landslide Hazard and Risk Assessment", Unisdr.org. [Online]. Available: https://www.unisdr.org/files/52828_03landslidehazardandriskassessment.pdf. [Accessed: 31- Aug- 2018].
  • F. Dai, C. Lee and Y. Ngai, "Landslide risk assessment and management: an overview", Engineering Geology, vol. 64, no. 1, pp. 65-87, 2002.
  • E. Topsakal and T. Topal, "Slope stability assessment of a re-activated landslide on the Artvin-Savsat junction of a provincial road in Meydancik, Turkey", Arabian Journal of Geosciences, vol. 8, no. 3, pp. 1769-1786, 2014.
  • A. Erener, A. Mutlu and H. Sebnem Düzgün, "A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)", Engineering Geology, vol. 203, pp. 45-55, 2016.
  • C. Ozgen, “An Invectigation of landslide at km:12+ 200 od Artvin-Savsat junction-Meydancik Provincial road”, PhD thesis, Middle East Technical University (2012).
  • P. Temel, “Evaluation of potential run-of river hydropower plant using multicriteria decision making in terms of environmental and social aspect”, PhD thesis, Middle East Technical University (2015).
  • Q. Ding, W. Chen and H. Hong, "Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping", Geocarto International, pp. 1-21, 2016.
  • C. Xu, X. Xu, F. Dai, J. Xiao, X. Tan and R. Yuan, "Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region", Journal of Earth Science, vol. 23, no. 1, pp. 97-120, 2012.
  • B. Pradhan and S. Lee, "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, vol. 25, no. 6, pp. 747-759, 2010.
  • L. Wang, M. Guo, K. Sawada, J. Lin and J. Zhang, "Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models", CATENA, vol. 135, pp. 271-282, 2015.
  • B. Pradhan, "A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS", Computers & Geosciences, vol. 51, pp. 350-365, 2013.
  • B. Feizizadeh, M. Roodposhti, T. Blaschke and J. Aryal, "Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping", Arabian Journal of Geosciences, vol. 10, no. 5, 2017.
  • D. Kumar, M. Thakur, C. Dubey and D. Shukla, "Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India", Geomorphology, vol. 295, pp. 115-125, 2017.
  • H. Saito, D. Nakayama and H. Matsuyama, "Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan", Geomorphology, vol. 109, no. 3-4, pp. 108-121, 2009.
  • E. Topsakal, "An Invectigation of landslide at km:12+ 200 od Artvin-Savsat junction-Meydancik Provincial road", PhD thesis, Middle East Technical University, (2012).
  • I. Yilmaz, "Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)", Computers & Geosciences, vol. 35, no. 6, pp. 1125-1138, 2009.
  • Weka 3: Data Mining Software in Java, [Online] Available: https://www.cs.waikato.ac.nz/ml/weka/[Accessed: 2018-04-10].
Yıl 2019, , 782 - 787, 01.10.2019
https://doi.org/10.16984/saufenbilder.479801

Öz

Kaynakça

  • A. Morales-Esteban, F. Martínez-Álvarez, A. Troncoso, J. Justo and C. Rubio-Escudero, "Pattern recognition to forecast seismic time series", Expert Systems with Applications, vol. 37, no. 12, pp. 8333-8342, 2010.
  • K. Asim, F. Martínez-Álvarez, A. Basit and T. Iqbal, "Earthquake magnitude prediction in Hindukush region using machine learning techniques", Natural Hazards, vol. 85, no. 1, pp. 471-486, 2016.
  • H. Cloke and F. Pappenberger, "Ensemble flood forecasting: A review", Journal of Hydrology, vol. 375, no. 3-4, pp. 613-626, 2009.
  • B. Bhattacharya and D. Solomatine, "Neural networks and M5 model trees in modelling water level–discharge relationship", Neurocomputing, vol. 63, pp. 381-396, 2005.
  • H. Langer, S. Falsaperla, A. Messina and S. Spampinato, "Perfomance of a new multistation alarm system for volcanic activity based on neural network techniques", in Second European Conference on Earthquake Engineering and Seismology, 2014.
  • J. Parra, O. Fuentes, E. Anthony and V. Kreinovich, "Use of Machine Learning to Analyze and – Hopefully – Predict Volcano Activity", Acta Polytechnica Hungarica, vol. 14, no. 3, 2017.
  • UNISDR: Landslide Hazard and Risk Assessment", Unisdr.org. [Online]. Available: https://www.unisdr.org/files/52828_03landslidehazardandriskassessment.pdf. [Accessed: 31- Aug- 2018].
  • F. Dai, C. Lee and Y. Ngai, "Landslide risk assessment and management: an overview", Engineering Geology, vol. 64, no. 1, pp. 65-87, 2002.
  • E. Topsakal and T. Topal, "Slope stability assessment of a re-activated landslide on the Artvin-Savsat junction of a provincial road in Meydancik, Turkey", Arabian Journal of Geosciences, vol. 8, no. 3, pp. 1769-1786, 2014.
  • A. Erener, A. Mutlu and H. Sebnem Düzgün, "A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)", Engineering Geology, vol. 203, pp. 45-55, 2016.
  • C. Ozgen, “An Invectigation of landslide at km:12+ 200 od Artvin-Savsat junction-Meydancik Provincial road”, PhD thesis, Middle East Technical University (2012).
  • P. Temel, “Evaluation of potential run-of river hydropower plant using multicriteria decision making in terms of environmental and social aspect”, PhD thesis, Middle East Technical University (2015).
  • Q. Ding, W. Chen and H. Hong, "Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping", Geocarto International, pp. 1-21, 2016.
  • C. Xu, X. Xu, F. Dai, J. Xiao, X. Tan and R. Yuan, "Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region", Journal of Earth Science, vol. 23, no. 1, pp. 97-120, 2012.
  • B. Pradhan and S. Lee, "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, vol. 25, no. 6, pp. 747-759, 2010.
  • L. Wang, M. Guo, K. Sawada, J. Lin and J. Zhang, "Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models", CATENA, vol. 135, pp. 271-282, 2015.
  • B. Pradhan, "A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS", Computers & Geosciences, vol. 51, pp. 350-365, 2013.
  • B. Feizizadeh, M. Roodposhti, T. Blaschke and J. Aryal, "Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping", Arabian Journal of Geosciences, vol. 10, no. 5, 2017.
  • D. Kumar, M. Thakur, C. Dubey and D. Shukla, "Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India", Geomorphology, vol. 295, pp. 115-125, 2017.
  • H. Saito, D. Nakayama and H. Matsuyama, "Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan", Geomorphology, vol. 109, no. 3-4, pp. 108-121, 2009.
  • E. Topsakal, "An Invectigation of landslide at km:12+ 200 od Artvin-Savsat junction-Meydancik Provincial road", PhD thesis, Middle East Technical University, (2012).
  • I. Yilmaz, "Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)", Computers & Geosciences, vol. 35, no. 6, pp. 1125-1138, 2009.
  • Weka 3: Data Mining Software in Java, [Online] Available: https://www.cs.waikato.ac.nz/ml/weka/[Accessed: 2018-04-10].
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Çevre Bilimleri
Bölüm Araştırma Makalesi
Yazarlar

Alev Mutlu 0000-0003-0547-0653

Furkan Goz 0000-0002-6726-3679

Kubra Koksal Bu kişi benim 0000-0002-4252-7295

Arzu Erener 0000-0002-9168-4254

Yayımlanma Tarihi 1 Ekim 2019
Gönderilme Tarihi 7 Kasım 2018
Kabul Tarihi 20 Mart 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Mutlu, A., Goz, F., Koksal, K., Erener, A. (2019). Landslide Susceptibility Assessment using Skyline Operator and Majority Voting. Sakarya University Journal of Science, 23(5), 782-787. https://doi.org/10.16984/saufenbilder.479801
AMA Mutlu A, Goz F, Koksal K, Erener A. Landslide Susceptibility Assessment using Skyline Operator and Majority Voting. SAUJS. Ekim 2019;23(5):782-787. doi:10.16984/saufenbilder.479801
Chicago Mutlu, Alev, Furkan Goz, Kubra Koksal, ve Arzu Erener. “Landslide Susceptibility Assessment Using Skyline Operator and Majority Voting”. Sakarya University Journal of Science 23, sy. 5 (Ekim 2019): 782-87. https://doi.org/10.16984/saufenbilder.479801.
EndNote Mutlu A, Goz F, Koksal K, Erener A (01 Ekim 2019) Landslide Susceptibility Assessment using Skyline Operator and Majority Voting. Sakarya University Journal of Science 23 5 782–787.
IEEE A. Mutlu, F. Goz, K. Koksal, ve A. Erener, “Landslide Susceptibility Assessment using Skyline Operator and Majority Voting”, SAUJS, c. 23, sy. 5, ss. 782–787, 2019, doi: 10.16984/saufenbilder.479801.
ISNAD Mutlu, Alev vd. “Landslide Susceptibility Assessment Using Skyline Operator and Majority Voting”. Sakarya University Journal of Science 23/5 (Ekim 2019), 782-787. https://doi.org/10.16984/saufenbilder.479801.
JAMA Mutlu A, Goz F, Koksal K, Erener A. Landslide Susceptibility Assessment using Skyline Operator and Majority Voting. SAUJS. 2019;23:782–787.
MLA Mutlu, Alev vd. “Landslide Susceptibility Assessment Using Skyline Operator and Majority Voting”. Sakarya University Journal of Science, c. 23, sy. 5, 2019, ss. 782-7, doi:10.16984/saufenbilder.479801.
Vancouver Mutlu A, Goz F, Koksal K, Erener A. Landslide Susceptibility Assessment using Skyline Operator and Majority Voting. SAUJS. 2019;23(5):782-7.

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