Sığ Sinir Ağları Modeli Yardımıyla Türkiye’de Refahiye İlçesinin Heyelan Duyarlılığının Haritalanması

Year 2020, Volume: 1 Issue: 2, 61 - 77, 30.09.2020

Sohaib K M Abujayyab , İsmail Rakıp Karaş

Abstract

Heyelanlar nüfus ve altyapı için sürekli bir tehlike oluşturmaktadır. Heyelan duyarlılığının haritalanması heyelan risklerini önlemek için önemli bir konudur. Bu çalışmanın amacı, Türkiye'nin Refahiye ilçesinde heyelan duyarlılık haritalaması için yüksek doğruluklu model üretmektir. Modelde heyelan duyarlılık haritalaması için sığ sinir ağlarını kullanılırken, uygun veriden gerekli faktörleri çıkarmak ve haritalama ve eğitim hesaplama süresini azaltmak için iki değişkenli Spearman sıra korelasyon testi kullanılmıştır. 21 mekansal faktörden 12'si, Spearman korelasyon testi kullanılarak ilgili faktörler olarak seçilmiştir. İlgili faktörler jeoloji, yollara uzaklık, jeolojik faylara olan uzaklık, su yollarına olan uzaklık, akış yönü, bakı, arazi kabartı, ısı yük endeksi, eğim / bakı dönüşümü, alan maruziyet indeksi, bileşik topografik indeks ve yüksekliktir. Oluşturulan veri kümesi, 10 katlı çapraz geçerlilik yöntemini kullanarak eğitim, doğrulama ve test veri kümelerine bölünmüştür. %86,3'lük genel doğruluk performansı elde edilen en iyi eğitim fonksiyonu (Trainlm)'dir. Geliştirilen NN modeli, IRIS kıyaslama veri seti kullanılarak test edildi ve lojistik regresyon algoritmasına göre daha yüksek performans gösterdi. Sonuç olarak, bu çalışmada heyelan duyarlılık haritalamasında sığ sinir ağları yöntemi başarıyla uygulanmış ve yöntem gelecekteki çalışmalar için önerilmiştir.

Keywords

CBS, Heyelan duyarlılık haritalaması, Sığ sinir ağları

Landslide Susceptibility Mapping Using Shallow Neural Networks Model at Refahiye District in Turkey

Abstract

Landslides represent a continuous hazard for population and infrastructure. Mapping the landslide susceptibility is an essential issue to avoid the landslides risks. The aim of this paper is to produce a high-accuracy model for landslide susceptibility mapping in Refahiye district in Turkey. The model employed shallow neural networks for landslide susceptibility mapping, while bivariate spearman correlation test was utilized to select the related factors to extract the appropriate data and reduce the computation time of training and mapping. 12 out of 21 spatial factors were selected as relevant factors using Spearman correlation test. Relevant factors are geology, distance from roads, distance from geological faults, distance from water streams, flow direction, aspect, hillshade, heat load index, slope/aspect transformation, site exposure index, compound topographic index, and elevation. The generated dataset was divided into training, validation, and testing datasets using 10-folds cross-validation method. The TrainIm was found to be the best training function with an overall accuracy of 86.3%. The developed NN model was tested using IRIS benchmark dataset and showed higher performance against the logistic regression algorithm. As a result, shallow neural networks method was successfully applied in landslide susceptibility mapping in this study and the method is recommended for future studies.

Keywords

GIS, Landslide susceptibility mapping, Shallow neural networks

References

• Can, A., Dagdelenler, G., Ercanoglu, M., & Sonmez, H. (2019). Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bulletin of Engineering Geology and the Environment, 78(1), 89–102.
• 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.
• McCormick, C. (2016, February 24). K-fold cross-validation, with Matlab code. Retrieved from https://chrisjmccormick.wordpress.com/2013/07/31/k-fold-cross-validation-with-matlab-code/#
• Dağ, S., & Bulut, F. (2012). Coğrafi Bilgi Sistemleri Tabanlı Heyelan Duyarlılık Haritalarının Hazırlanmasına Bir Örnek: Çayeli (Rize, KD Türkiye). Jeoloji Mühendisliği Dergisi, 36(1), 35–62.
• Felicísimo, Á., Cuartero, A., Remondo, J., & Quirós, E. (2013). Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides, 10(2), 175–189.
• Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios. Sustainability, 10(10), 3421, doi: 10.3390/su10103421
• JAXA. (2019, April 9). About ALOS - Overview and Objectives. Retrieved from https://www.eorc.jaxa.jp/ALOS/en/about/ about_index.htm
• Kim, D. E., & Gofman, M. (2018, January). Comparison of shallow and deep neural networks for network intrusion detection. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 2018. pp. 204–208. IEEE.
• 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), 1194. doi: 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.
• 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), 171–191.
• 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.
• Pitasi, A. (2016). Susceptibility analysis to identify the zones potentially exposed to rapid flowslide risk (Doctoral dissertation). Mediterranea University of Reggio Calabria, Italy. https://doi.org/10.13140/RG.2.2.11505.28004
• 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.
• 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.
• Duman, T. Y., Çan, T., & Emre, Ö. (2011). 1:1,500,000 Scale Landslide Inventory Map of Turkey. General Directorate of Mineral Research and Exploration, Special Publication Series-27, Ankara, Turkey.
• Tso, G. K. F., & Yau, K. K. W. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761–1768.
• Turner, A. K., & Schuster, R. L. (1996). Landslides: investigation and mitigation. Washington: National academy Press.
• Vakhshoori, V., Pourghasemi, R. H., Zare, M., & Blaschke, T. (2019). Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms. Water, 11(11), 2292, doi: 10.3390/w11112292.
• Valencia Ortiz, J. A., & Martínez-Graña, A. M. (2018). A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia). Geomatics, Natural Hazards and Risk, 9(1), 1106–1128.
• Wang, L., Jia, Y., Yao, Y., & Xu, D. (2019). Accuracy Assessment of Land Use Classification Using Support Vector Machine and Neural Network for Coal Mining Area of Hegang City, China. Nature Environment and Pollution Technology, 18(1), 335–341.
• Wise, S. (2011). Cross-validation as a means of investigating DEM interpolation error. Computers & Geosciences, 37(8), 978–991.
• Yalcin, A, Reis, S., Aydinoglu, A. C., & Yomralioglu, T. (2011). A 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, 85(3), 274–287.
• Yalcin, Ali. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72(1), 1–12.
• Yilmaz, I. (2009). 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.
• Yıldırım, Ü., & Güler, C. (2016). Identification of suitable future municipal solid waste disposal sites for the Metropolitan Mersin (SE Turkey) using AHP and GIS techniques. Environmental Earth Sciences, 75(2), 101, doi: 10.1007/s12665-015-4948-8.
• Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., & Zhang, S. (2018). Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors, 18(11), 3717, doi: 10.3390/s18113717
• Zhang, X.-D. (2020). A Matrix Algebra Approach to Artificial Intelligence. Springer, Singapore.