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Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)

Yıl 2025, Cilt: 36 Sayı: 1
https://doi.org/10.18400/tjce.1290125

Öz

In the current investigation, a Geographic Information System (GIS) and machine learning-based software were employed to generate and compare landslide susceptibility maps (LSMs) for the city center of Tokat, which is situated within the North Anatolian Fault Zone (NAFZ) in the Central Black Sea Region of Turkey, covering an area of approximately 2003 km2. 294 landslides were identified within the study area, with 258 (70%) randomly selected for modeling and the remaining 36 (30%) used for model validation. Three distinct methodologies were used to generate LSMs, namely Frequency Ratio (FR), Logistic Regression (LR), and Deep Learning (DL), using nine parameters, including slope, aspect, curvature, elevation, lithology, rainfall, distance to fault, distance to road, and distance to stream. The susceptibility maps produced in this study were categorized into five classes based on the level of susceptibility, ranging from very low to very high. This study used the area under receiver operating characteristic curve (AUC-ROC), overall accuracy, and precision methods to validate the results of the generated LSMs and compare and evaluate the performance. DL outperformed all validation methods compared to the others. Finally, it is concluded that the generated LSMs will assist decision-makers in mitigating the damage caused by landslides in the study area.

Kaynakça

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Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)

Yıl 2025, Cilt: 36 Sayı: 1
https://doi.org/10.18400/tjce.1290125

Öz

In the current investigation, a Geographic Information System (GIS) and machine learning-based software were employed to generate and compare landslide susceptibility maps (LSMs) for the city center of Tokat, which is situated within the North Anatolian Fault Zone (NAFZ) in the Central Black Sea Region of Turkey, covering an area of approximately 2003 km2. 294 landslides were identified within the study area, with 258 (70%) randomly selected for modeling and the remaining 36 (30%) used for model validation. Three distinct methodologies were used to generate LSMs, namely Frequency Ratio (FR), Logistic Regression (LR), and Deep Learning (DL), using nine parameters, including slope, aspect, curvature, elevation, lithology, rainfall, distance to fault, distance to road, and distance to stream. The susceptibility maps produced in this study were categorized into five classes based on the level of susceptibility, ranging from very low to very high. This study used the area under receiver operating characteristic curve (AUC-ROC), overall accuracy, and precision methods to validate the results of the generated LSMs and compare and evaluate the performance. DL outperformed all validation methods compared to the others. Finally, it is concluded that the generated LSMs will assist decision-makers in mitigating the damage caused by landslides in the study area.

Kaynakça

  • Hungr, O., Leroueil, S., Picarelli, L., The Varnes classification of landslide types, an update. Landslides, 11, 167-194, 2014.
  • Jakob, M., Chapter 14 - Landslides in a changing climate, Landslide Hazards, Risks, and Disasters (Second Edition). Hazards and Disasters Series, 505-579, 2022.
  • AFAD, Landslide-Rockfall Basic Guide. Ministry of Interior Disaster and Emergency Management Presidency, Ankara, Turkey, 2018.
  • Yalcin, A., Reis, S., Aydinoglu, A.C., Nadirli, S.A., 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, 2011.
  • Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112, 42-66, 21, 2012.
  • Bhandari, B.P., Dhakal, S., Topographical and geological factors on gully-type debris flow in Malai River catchment, Siwaliks, Nepal. Journal of Nepal Geological Society, 59, 2019.
  • Gariano, S.L., Melillo, M., Peruccacci, S., How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering? Natural Hazards, 100, 655-670, 2020.
  • Kumi-Boateng, B., Peprah, M.S., Larbi, E.K., Prioritization of forest fire hazard risk simulation using hybrid grey relativity analysis (HGRA) and fuzzy analytical hierarchy process (FAHP) coupled with multicriteria decision analysis (MCDA) techniques - a comparative study analysis. Geodesy and Cartography 47, 3, 2021.
  • Wubalem, A., Landslide Inventory, Susceptibility, Hazard and Risk Mapping. Submitted: June 25th, 2021 Reviewed: September 17th, 2021 Published: November 20th, 2021.
  • Shano, L., Raghuvanshi, T.K., Meten, M., Landslide susceptibility evaluation and hazard zonation techniques - a review. Geoenvironmental Disasters, 7, 18, 2020.
  • Turan, I.D., Ozkan, B., Turkes, M., Deniz, O., Landslide susceptibility mapping for the Black Sea Region with spatial fuzzy multi-criteria decision analysis under semi-humid and humid terrestrial ecosystems. Theoretical and Applied Climatology, 140, 1233-1246, 2020.
  • Marker, B.R., Hazard and Risk Mapping, First Online: 01 January 2016, Citations Part of the Encyclopedia of Earth Sciences Series book series (EESS), 2016.
  • Reichenbach, P., Rossi, M., Malamud, B.D., Mihir, M., Guzzetti, F., A review of statistically-based landslide susceptibility models. Earth-Science Reviews 180, 60-91, 2018.
  • Pradhan, B., A comparative study on the predictive ability of the decision tree, support vector machine, and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computer and Geosciences, 51, 350-365, 2013.
  • Chen, T., Niu, R., Du, B., Landslide spatial susceptibility mapping using GIS and remote sensing techniques: a case study in Zigui County, the Three Georges reservoir, China. Environmental Earth Sciences, 73, 5571-5583, 2015.
  • Saleem, N., Huq, E., Twumasi, N.Y.D., Javed, A., Sajjad, A., Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review. Geospatial Approaches to Landslide Mapping and Monitoring, 8, 545, 2019.
  • Pourghasemi, H.R., Yansari, Z.T., Panagos, P., Pradhan, B., Analysis and evaluation of landslide susceptibility: a review of articles published during 2005-2016 (periods of 2005-2012 and 2013-2016). Arabian Journal of Geosciences, 11, 193, 2018.
  • Bui, D.T., Tuan, T.A., Klempe, H., Pradhan, B., Revhaug, I., Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13, 361-378, 2016.
  • Yong, C., Jinlong, D., Fei, G., Bin, T., Tao, Z., Hao, F., Li, W., Qinghua, Z., Review of landslide susceptibility assessment based on knowledge mapping. Stochastic Environmental Research and Risk Assessment, 36, 2399-2417, 2022.
  • NVİ, Tokat Provincial Directorate of Population and Citizenship, Tokat, Turkey, 2022.
  • Ayele, S., Raghuvanshi, T.K., Kala, P.M., Application of Remote Sensing and GIS for Landslide Disaster Management: A Case from Abay Gorge, Gohatsion–Dejen Section, Ethiopia. Landscape Ecology and Water Management, 15-32, 2014.
  • Althuwaynee, O.F., Pradhan, B., Park, H.J., Lee, J.H., A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21-36, 2014.
  • Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Bui, D.T., Duan, Z., Li, S., Zhu, A., GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena, 164, 135-149, 2018.
  • Lee, S., Current and Future Status of GIS-based Landslide Susceptibility Mapping: A Literature Review. Korean Journal of Remote Sensing, 1, 179-193, 2019.
  • Meng, Q., Miao, F., Zhen, J., Wang, X., Wang, A., Peng, Y., Fan, Q., GIS-based landslide susceptibility mapping with logistic regression, analytical hierarchy process, and combined fuzzy and support vector machine methods: a case study from Wolong Giant Panda Natural Reserve, China. Bulletin of Engineering Geology and the Environment, 75, 923-944, 2016.
  • Kavzoglu, T., Teke, A., Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost). Arabian Journal for Science and Engineering, 47, 7367-7385, 2022.
  • Zhang, H., Song, Y., Xu, S., He, Y., Li, Z., Yu, X., Liang, Y., Wu, W., Wang, Y., 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. Computer and Geosciences, 158, 2022.
  • Chung, C.F., Fabbri, A.G., Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30, 451-472, 2003.
  • Ohlmacher, G.C., Davis, J.C., Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69, 3-4, 331-343, 2003.
  • Nachappa, T.G., Kienberger, S., Meeana, S.R., Hölbling, D., Blaschke, T., Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 572-600, 2020.
  • Ercanoglu, M., Gokceoglu, C., Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75:229-250, 2004.
  • MTA, General Directorate of Mineral Research and Exploration, Ankara, Turkey, 2020.
  • Tsangaratos, P., Ilia, I., Comparison of logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. Catena, 145, 164-179, 2016.
  • Sun, D., Wen, H., Wang, D., Xu, J., A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology, 362, 2020.
  • Conforti, M., Letto, F., Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis. Geosciences, 11, 8, 2021.
  • Siddique, T., Pradhan, S.P., Stability and sensitivity analysis of Himalayan road cut debris slopes: an investigation along NH-58, India. Natural Hazards, 93, 577-600, 2018.
  • El-Magd, S.A.A., Eldosouky, A.M., An improved approach for predicting the groundwater potentiality in the low desert lands; El-Marashda area, Northwest Qena City, Egypt. Journal of African Earth Sciences, 179, 2021.
  • Qin, Z., Lai, Y., Tian, Y., Study on failure mechanism of a plain irrigation reservoir soil bank slope under wind wave erosion. Natural Hazards, 109, 567-592, 2021.
  • Akgun, A., GIS-based erosion and landslide susceptibility assessment of the Ayvalık and surroundings. Ph.D. Thesis, Dokuz Eylül University, İzmir, Turkey, 2007.
  • Dai, F.C., Lee, C.F., Landslide characteristics and slope instability modeling using GIS. Lantau Island, Hong Kong. Geomorphology, 42, 213-228, 2002.
  • Liu, Q., Tang, A., Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology. Geocarto International, 14547-14569, 2022.
  • Gokceoglu, C., Sonmez, H., Nefeslioglu, H.A., Duman, T.Y., Can, T., The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Engineering Geology, 81, 1, 65-83, 2005.
  • Duman, T.Y., Can, T., Gokceoglu, C., Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology, 51, 241-256, 2006.
  • Gokceoglu, C., Ercanoglu, M., Uncertainties on the parameters employed to prepare landslide susceptibility maps. Bulletin of Earth Sciences Application and Research Centre of Hacettepe University Critical Review, 2001.
  • Terranova, O.G., Garaino, S.L., Bruno, C., Greco, R., Pellegrino, A.D., Iovine, G.G.R., Landslide-risk scenario of the Costa Viola mountain ridge (Calabria, Southern Italy). Journal of Maps, 12, 261-270, 2016.
  • Clerici, A., Perego, S., Vescovi, P., A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology, 48 (4): 349-364, 2002.
  • Ayalew, L., Yamagishi, H., The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains (Central Japan). Geomorphology, 65, 15-31,2005.
  • Ermini, L., Catani, F., Casagli, N., Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66, 327-343, 2005.
  • Luzi, L., Pergalani, F., Slope Instability in Static and Dynamic Conditions for Urban Planning: the ‘Oltre Po Pavese’ Case History (Regione Lombardia- Italy), Natural Hazards, 20, 57-82, 1999.
  • Wachal, D.J., Hudak, P.F., Mapping landslide susceptibility in Travis County. Texas, USA, GeoJournal, 51, 245-253, 2000.
  • Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., Rahmati, O., Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto International, 33, 927-941, 2017.
  • Huang, F., Yao, C., Liu, W., Li, Y., Liu, X., Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine. Geomatics, Natural Hazards and Risk, 9, 919-938, 2018.
  • Khan, H., Shafique, M., Khan, M.A., Bacha, M.A., Shah, S.U., Calligaris, C., Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. The Egyptian Journal of Remote Sensing and Space Sciences, 22, 11-24, 2018.
  • Gholami, M., Ghachkanlu, M.E., Khosravi, K., Pirasteh, S., Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. Journal of Earth System Sciences, 128, 42, 2019.
  • Lee, S., Ryu, J., Won, J., Park, H., Determination and application of the weight for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71-80, 2004.
  • Ataol, M., Yesilyurt, S., Identification of landslide risk zones along the Çankırı-Ankara (Between Akyurt and Çankırı) state road. Journal of Geography, 0, 51-69, 2014.
  • Pham, B.T., Bui, D.T., Indra, P., Dholakia, M., Landslide Susceptibility Assessment at a Part of Uttarakhand Himalaya, India using GIS-based Statistical Approach of Frequency Ratio Method. International Journal of Engineering Research and Technology, 11, 338-344, 2015.
  • Demir, G., GIS-Based Landslide Susceptibility Mapping for a Part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey). Catena, 183,104211, 2019.
  • Silalahi, F.E.S., Arifianti, P.Y., Hidayat, F., Landslide susceptibility assessment using frequency ratio model in Bogor. West Java, Indonesia. Geoscience Letter, 6, 10, 2019.
  • Erener, A., Lacasse, S., Landslide susceptibility mapping using GIS, 28th Asian Conference on Remote Sensing, 2007, Kuala Lumpur, Malesia, 2007.
  • Demir, G., Landslide Susceptibility Mapping by Using Statistical Analysis in the North Anatolian Fault Zone (NAFZ) on the Northern Part of Suşehri Town, Turkey. Natural Hazard, 92, 133-154, 2018.
  • Karaman, M.O., Cabuk, S.N., Pekkan, E., Utilization of Frequency Ratio Method for the Development of Landslide Susceptibility Maps: Karaburun Peninsula Case, Turkey. Research Square, 2022.
  • Hilbe, J.M., Logistic Regression Models. 1st edition, New York, 2009.
  • Kleinbaum D.G., Kupper, L.L., Muller, K.E., Applied regression analysis and other multivariable methods. 3rd Edition, Duxbury Press, California, 798, 1998.
  • Atkinson, P.M., Massari, R., Generalized Linear Modelling of Susceptibility to Attributes. Engineering Geology, 32, 81-100, 1998.
  • Bui, D.T., Lofman, O., Revhaug, I., Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards, 59, 1413-144, 2011.
  • Das, I., Sahoo, S., van Westen, C., Stein, A., Hacjk, R., Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology, 114, 1, 627-637, 2010.
  • Lang, S., Marquez, F.M., Beckham, C., Hall, M., Frank, E., WekaDeeplearning4j: A deep learning package for Weka based on Deeplearning4j. Knowledge-Based Systems, 178, 48-50, 2019.
  • Bengio, Y., Deep learning of representations: looking forward, Part of the Lecture Notes in Computer Science Book Series. Montreal University, Canada, 1-2, 2013.
  • LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature, 521, 436-444, 2015.
  • Schmidhuber, J., Deep learning in neural networks: An overview. Neural Networks, 61, 85-117, 2015.
  • Goodfellow, I., Bengio, Y., Courville, A., Deep learning. MIT Press, 2016.
  • Jordan, M.I., Mitchell, T.M., Machine learning: Trends, perspectives, and prospects. Science, 349, 255-260, 2015.
  • Clark, W.A.V., Hosking, P.L., Statistical Methods for Geographers, John Wiley and Sons. 518, New York, 1991.
  • Akinci, H.A., Akinci, H., Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya). Turkey Earth Science Informatics, 16(1):397-414, 2023.
  • Ye, P., Yu, B., Chen, W., Liu, K., Ye, L., Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province. China Nat. Hazards, 113:965-995, 2022.
  • Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Moezzi, K.D., Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz watershed, Iran. In Terrigenous Mass Movements; Pradhan, B., Buchroithner, M., Eds.; Springer: Berlin/Heidelberg, Germany, pp. 23-49. ISBN 978-3-642-25495-6, 2012.
  • Yilmaz, I., Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Comput Geosci 35(6):1125–1138, 2009.
  • Hoo, H.Z, Candlish, J., Teare, D., What is an ROC curve? Emergency Medicine Journal, 34, 6, 2017.
  • Aggarwal, C. C., Neural Networks and Deep Learning, Vol. 497, Springer, 2018.
  • Azarafza, M., Akgun, H., Atkinson, P.M., Derakhshani, R., Deep learning-based landslide susceptibility mapping. Sci Rep 11:24112, 2021.
Toplam 81 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Ayhan Başalan 0000-0002-1342-1336

Gökhan Demir 0000-0002-3734-1496

Erken Görünüm Tarihi 24 Temmuz 2024
Yayımlanma Tarihi
Gönderilme Tarihi 1 Mayıs 2023
Yayımlandığı Sayı Yıl 2025 Cilt: 36 Sayı: 1

Kaynak Göster

APA Başalan, A., & Demir, G. (2024). Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey). Turkish Journal of Civil Engineering, 36(1). https://doi.org/10.18400/tjce.1290125
AMA Başalan A, Demir G. Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey). tjce. Temmuz 2024;36(1). doi:10.18400/tjce.1290125
Chicago Başalan, Ayhan, ve Gökhan Demir. “Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)”. Turkish Journal of Civil Engineering 36, sy. 1 (Temmuz 2024). https://doi.org/10.18400/tjce.1290125.
EndNote Başalan A, Demir G (01 Temmuz 2024) Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey). Turkish Journal of Civil Engineering 36 1
IEEE A. Başalan ve G. Demir, “Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)”, tjce, c. 36, sy. 1, 2024, doi: 10.18400/tjce.1290125.
ISNAD Başalan, Ayhan - Demir, Gökhan. “Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)”. Turkish Journal of Civil Engineering 36/1 (Temmuz 2024). https://doi.org/10.18400/tjce.1290125.
JAMA Başalan A, Demir G. Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey). tjce. 2024;36. doi:10.18400/tjce.1290125.
MLA Başalan, Ayhan ve Gökhan Demir. “Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey)”. Turkish Journal of Civil Engineering, c. 36, sy. 1, 2024, doi:10.18400/tjce.1290125.
Vancouver Başalan A, Demir G. Comparative Analysis of Frequency Ratio, Logistic Regression and Deep Learning Methods for Landslide Susceptibility Mapping in Tokat Province on the North Anatolian Fault Zone (Turkey). tjce. 2024;36(1).