Application of quantitative methods for the assessment of landslide susceptibility of the Aghsuchay river basin
Problem statement. Azerbaijan is making a lot of efforts to reduce the impact of dangerous geological processes on natural geosystems, but they still cause huge damage. To a greater extent, the region of the Greater Caucasus, namely the southern slope, is subject to such processes, where the whole range of dangerous geological processes occurs: earthquake (7-8 b and above), landslides, landslides, screes, mudflows, etc. All of them are large-scale processes in terms of damage - they affect large areas and lead to economic losses.
Purpose - to identify the main factors of the formation and spread of landslides in the basin of one of the most mudflow-bearing rivers not only in Azerbaijan, but also in the South Caucasus - the Agsuchay river, identify the conditions for their formation, assess the risk of the territory's susceptibility to landslide processes, as well as ways to prevent and protect.
Research method. To assess landslide susceptibility and create maps of the potential development of landslides in the basin of the Agsuchay river we used the Frequency Ratio method (FR).
Research results. For minimize damage from landslides on the example of the Agsuchay river basin a detailed study of the factors (hypsometry, slope angles (slope steepness) was carried out by us. Also slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion network, distance to roads and land use) that determine the development of landslide processes with taking into account the mechanism of their development, as well as an analysis of the obtained values of landslide susceptibility and their potential development was studied. In the ArcGIS software environment, using the “Raster Calculator” spatial analysis tool, summing up each landslide factor multiplied by its weights, a map of the landslide susceptibility of the Agsuchay river basin was obtained.
In the river basin Agsuchay we identified over 120 landslide areas. Most of the landslides were recorded along the Baskal tectonic cover, the Steppe Plateau, as well as on the slopes of the Langyabiz ridge, and also partially on the slopes of the Nialdag ridge.
Conclusion. Using the natural boundary classification method in the ArcGIS software environment, the study area was divided into five landslide potential zones: very low, low, medium, high, and very high. The result of the analysis showed that zones with very low, low, medium, high and very high landslide development potential are: 13.75; 24.48; 31.51; 20.51 and 9.74% of the study area, respectively.
Ultimately, the reliability of the obtained models was evaluated using AUC ROC (area under the error curve) analysis, which showed high performance of the method used (82%). Due to the high reliability, the method used can be used to assess the landslide susceptibility not only of the territory of Azerbaijan, but of similar regions of the Alpine-Himalayan belt.
Akgun, A., Dag, S., Bulut F. (2008). Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood frequency ratio and weighted linear combination models. Environmental Geology. 54(6), 1127–1143.
Akgun, A., Needet, T. (2010). Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by mul-ti criteria decision analysis. Environmental Earth Science. 61, 595–611.
Aleotti, P., Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and Environment. 58, 21-44.
Arabameri, A., Pradhan, B., Rezaei, K., Lee, C.-W. (2019). Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sensing. 11 (9). URL: https://doi.org/10.3390/rs11090999.
Baynes, F.J., Lee, I.K., Stewart, I.E. (2002). A study of the accuracy and precision of some landslide risk analyses. Australia: Geomech. 37, 149-156.
Berhane, G., Tadesse, K. (2021). Landslide susceptibility zonation mapping using statistical index and landslide susceptibility analysis methods: a case study from Gindeberet district, Oromia Regional State, Central Ethiopia. Journal of African Earth Sciences. 180. URL: https://doi.org/10.1016/j.jafrearsci.2021.104240.
Brardinoni, F., Slaymaker, О., Hassan, М.А. (2003). Landslide inventory in a rugged forested watershed: a compar-ison between air-photo and field survey data. Geomorphology. 54, 179-196.
Cantarino, I., Carrion, M.A., Goerlich, F., Martinez Ibañez, V.A (2019). ROC analysis-based classification method for landslide susceptibility maps. Landslides. 16, 265–282. URL: https://doi.org/10.1007/s10346-018-1063-4.
Castellanos Abella, E.A., Van Westen, C.J. (2008). Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba. Geomorphology. 94, 453–466.
Cervi, F., Berti, M., Borgatti, L., Ronchetti, F., Manenti, F., Corsini, A. (2010). Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy). Landslides. 7, 433–444.
Chacon, J., Irigaray, C., Fernandez, T. et al. (2006). Engineering geology maps: landslides and geographical in-formation systems. Bulletin of Engineering Geology and the Environment. 65 (4), 341-411.
Ciurleo M., Cascini L., Calvello M. (2017). A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils. Engineering Geology. 223 (7). 71–81. URL: https://doi.org/10.1016/j.enggeo.2017.04.023.
Constantin, M., Bednarik, M., Jurchescu, M.C., Vlaicu M. (2011). Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environmental Earth Sci-ence. 63, 397-406. https://doi.org/10.1007/s12665-010-0724-y.
Corominas, J., van Westen, C.J., Frattini, P. et al. (2014). Recommendations for the quantitative analysis of land-slide risk. B. Eng. Geol. Environ. 73, 209-263.
Duong, V.B., Fomenko, I.K., Nguyen, T.K., ThiHong, L.Vi., Zerkal, O.V., Hong, D.Vu. (2022) Application of GIS-based bivariate statistical methods for landslide potential assessment in Sapa, Vietnam. Bulletin of the Tomsk Pol-ytechnic University [TPU Bulletin]. Geo Assets Engineering. 333 (4), 126-140. URL: http://earchive.tpu.ru/handle/11683/70765.
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., Savage, W.Z. (2008). Guidelines for landslide suscepti-bility, hazard and risk zoning for land use planning. Engineering Geology. 102 (3), 85–98. URL: https://doi.org/10.1016/j.enggeo.2008.03.022.
Froude, M.J., Petley, D.N. (2018). Global fatal landslide occurrence 2004 to 2016. Natural Hazards and Earth System Sciences. 18 (8), 2161–2181. URL: https://doi.org/10.5194/nhess-18-2161-2018.
Gaidzik, K., Ramírez-Herrera, M.T. (2021). The importance of input data on landslide susceptibility mapping. Sci-entific Reports. 11 (1). URL: https://doi.org/10.1038/s41598-021-98830-y.
Getachew, N., Meten, M. (2021). Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenvironmental Disasters. 8 (1). URL: https://doi.org/10.1186/s40677-021-00177-z.
Gokceoglu, C., Sönmez, H., Nefeslioglu, H.A., Duman, T.Y., Can, T. (2005). Kuzulu landslide (Sivas, Turkey) and landslide susceptibility map of its near vicinity. Engineering Geology. 81 (1), 65-83.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F. (2005). Probabilistic landslide hazard assess-ment at the basin scale. Geomorphology. 72 (1), 272–299. URL: https://doi.org/10.1016/j.geomorph.2005.06.002
Haque, U., Da Silva, P.F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., Wilopo, W., Andersen, P., Lu, P., Lee, J., Yama-moto, T., Keellings, D., Wu, J.-H., Glass, G.E. (2019). The human cost of global warming: deadly landslides and their triggers (1995–2014). Science of The Total Environment. 682, 673–684. URL: https://doi.org/10.1016/j.scitotenv.2019.03.415.
Hong, H., Pradhan, B., Sameen, M.I. et al. (2018). Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides. 15 (4), 753–772.
Kose, D.D., Turk, T. (2019). GIS-based fully automatic landslide susceptibility analysis by weight-of-evidence and frequency ratio methods. Physical Geography. 40 (5), 481–501. URL: https://doi.org/10.1080/02723646.2018.1559583.
Lee, S., Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4, 33–41.
Lee, S., Ryu, J.H., Min, K.D., Won, J.S. (2019). Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Process Landforms. 27, 1361–1376.
Mandal, S., Mondal, S. (2019). Statistical approaches for landslide susceptibility assessment and prediction. Swit-zerland: Springer International Publishing, 200.
McColl, S.T. (2015). Chapter 2. Landslide causes and triggers // Landslide hazards, risks and disasters (J.F. Shroder, T. Davies. Eds.). Boston: Academic Press, 17–42. URL: https://doi.org/10.1016/B978-0-12-396452-6.00002-1.
Mersha, T., Meten, M. (2020). GIS-based landslide susceptibility mapping and assessment using bivariate statisti-cal methods in Simada area, northwestern Ethiopia. Geoenvironmental Disasters. 7 (1). URL: https://doi.org/10.1186/s40677-020-00155-x.
Nahayo, L., Mupenzi, C., Habiyaremye, G., Kalisa, E., Udahogora, M., Nzabarinda, V., Li, L. (2019). Landslides hazard mapping in Rwanda using bivariate statistical index method. Environmental Engineering Science. 36 (8), 892–902. URL: https://doi.org/10.1089/ees.2018.0493.
Nefeslioglu, H.A., Duman, T.Y., Durmaz, S. (2008). Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology. 94 (3), 401–418. URL: https://doi.org/10.1016/j.geomorph.2006.10.036.
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. Engineer-ing Geology. 97, 171–191.
Nefeslioglu, H.A., Sezer, E., Gokceoglu, C., Bozkir, A.S., Duman, T.Y. (2010). Assessment of landslide susceptibility by decision trees in the Metropolitan Area of Istanbul, Turkey. Mathematical Problems in Engineering. Article ID 901095, 15. https://doi.org/10.1155/2010/901095.
Oh, H.-J., Lee, S. (2011). Cross-application used to validate landslide susceptibility maps using a probabilistic model from Korea. Environmental Earth Science. 64, 395-409.
Oh, H.-J., Lee, S., Hong, S.-M. (2017). Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. Journal of Sensors. URL: https://doi.org/10.1155/2017/3730913.
Ozdemir, A. (2009). Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using con-ditional probability approach in GIS. Environmental Geology. 57, 1675-1686.
Pourghasemi, H.R., Mohammady, M., Pradhan, B. (2012). Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. CATENA. 97, 71–84.
Pourghasemi, H.R., Moradi, H.R., Aghda, S.F. (2013). Landslide susceptibility mapping by binary logistic regres-sion, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards. 69 (1), 749–779.
Pradhan, B. (2013). 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 Geosciences. 51, 350–365.
Ram Mohan, V., Jeyaseelan, A., Naveen Raj, T., Narmatha, T., Jayaprakash, M. (2011). Landslide susceptibility mapping using frequency ratio method and GIS in south eastern part of Nilgiri District, Tamilnadu, India. Interna-tional Journal Geomatics and Geoscience. 1 (4), 951-961.
Reichenbach, P., Rossi, M., Malamud, B.D., Mihir, M., Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews. 180, 60–91. URL: https://doi.org/10.1016/j.earscirev.2018.03.001.
Roccati, A., Paliaga, G., Luino, F., Faccini, F., Turconi, L. (2021). GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land. 10, (2). URL: https://doi.org/10.3390/land10020162.
Saito, H., Nakayama, D., Matsuyama, H. (2009). Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan. Geomorphology. 109, 108–121.
Schlögel, R., Doubre, C., Maletl, J.-P. et al. (2015). Landslide deformation monitoring with ALOS/PALSAR image-ry: a DInSAR geomorphological interpretation method. Geomorphology. 231, 314-330.
Sezer, E.A., Pradhan, B., Gokceoglu, C. (2011). Manifestation of an adaptive neuro-fuzzy model on landslide sus-ceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications. 38 (7), 8208-8219.
Sestraș, P., Bilașco, Ș., Roșca, S., Naș, S., Bondrea, M.V., Gâlgău, R., Vereș, I., Sălăgean, T., Spalević, V., Cîmpeanu, S.M. (2019). Landslides susceptibility assessment based on GIS statistical bivariate analysis in the hills surround-ing a metropolitan area. Sustainability. 11 (5). URL: https://doi.org/10.3390/su11051362.
Shano, L., Raghuvanshi, T.K., Meten, M. (2020). Landslide susceptibility evaluation and hazard zonation tech-niques–a review. Geoenvironmental Disasters. 7 (1). URL: https://doi.org/10.1186/s40677-020-00152-0.
Shannon, C.E. (1950). Prediction and entropy of printed English. The Bell System Technical Journal. 30, 50-64.
Shaw, S.C., Vaugeois, L.M. (1999). Comparison of GIS-based Models of Shallow Landsliding for Application to Watershed Management. Seattle: State of Washington Timber/Fish/Wildlife Publication 118, TFW-PR10-99-001, 132.
Süzen, M.L., Doyuran, V. (2004). A comparison of the GIS based landslide susceptibility assessment methods: mul-tivariate versus bivariate. Environmental Geology. 45 (5), 665–679. URL: https://doi.org/10.1007/s00254-003-0917-8.
Tarikhazer, S.A. (2020). The geographical prerequisites for the identification and prevention of dangerous geo-morphological processes in the mountain geosystems of the Alpine-Himalayan belt (on the example of the Major Caucasus of Azerbaijan). Bulletin of Dnipropetrovsk University. Geology, Geography and Geoecology, 1. 176-187. DOI https://doi.org/10.15421/112016
Tarikhazer, S.A. (2022). Assessment of ecological strength and risk of geosystems of the north-eastern slope of the Great Caucasus (within Azerbaijan). Bulletin of V.N. Karazin Kharkiv National University, series Geology. Geog-raphy. Ecology. 56. Pp. 264-276 https://doi.org/10.26565/2410-7360-2022-56-20
Tiranti, D., Cremonini, R. (2019). Editorial: landslide hazard in a changing environment. Frontiers in Earth Sci-ence. 7 (3). URL: https://doi.org/10.3389/feart.2019.00003.
Van Westen, C.J. (1997). Statistical landslide susceptibility analysis. ILWIS 2.1 for Windows application guide. Enschede: ITC Publ., 73–84.
Van Westen, C.J., Van Asch, T.W.J., Soeters, R. (2006). Landslide hazard and risk zonation – why is it still so diffi-cult? Bulletin of engineering geology and the environment. 65 (2), 167–184. URL: https://doi.org/10.1007/s10064-005-0023-0.
Van Westen, C.J., Castellanos E., Kuriakose S.L. (2008). Spatial data for landslide susceptibility, hazard, and vul-nerability assessment: An overview. Engineering Geology. 102, 112-131.
Xiong, J., Li, J., Zhang, H., Sun, M., Cheng, W. (2019) Quantitative Hazard Assessment of Landslides Using the Le-venburg–Marquardt Back Propagation Neural Network Method in a Pipeline Area. Geosciences. 9 (10), 1-23. URL: https://doi.org/10.3390/geosciences9100449.
Yalcin, A., Reis, S., Aydinoglu, A.C., Yomralioglu, T. A (2011). 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. URL: https://doi.org/10.1016/j.catena.2011.01.014.
Yesilnacar, E.K. (2005). The application of computational intelligence to landslide susceptibility mapping in Tur-key [Candidate’s dissertation]. Department of Geomatics the University of Melbourne, 423.
Belov A.V., Sokolova L.P. (2012). Nekotorye aspekty ekologicheskih riskov prirodopol'zovaniya na yuge Bajkal'skoj Sibiri [Some aspects of environmental risks of nature management in the south of Baikal Siberia]. Ge-ografiya i prirodnye resursy [Geography and natural resources], 4, 90-97 [in Russian].
Budagov B.A., Lilienberg D.A., Shirinov N.Sh. (1960). Istoriya razvitiya gidrograficheskoj seti Yugo-Vostochnogo Kavkaza [History of the development of the hydrographic network of the South-Eastern Caucasus]. Izv. AN Azeb. SSR, Seriya geol.-geograf. nauk [Proceedings of AS of Azerbaijan. SSR, Series geol.-geographer. Sciences], 1, 3-7 [in Russian].
Budagov, B.A. (1993). Gravitacionnaya morfoskul'ptura [Gravitational morphosculpture]. V knige: "Rel'ef Azerbajdzhana" [In book: Relief of Azerbaijan]. Baku: Elm, 22-28 [in Russian].
Leonova, A.V., Strokova, L.A., Nikitenkov, A.N. (2021). Ocenka opolznevyh processov na territorii g. Tomska s ispol'zovaniem GIS-tekhnologij [Assessment of landslide processes on the territory of Tomsk by using GIS Tech-nologies]. Vestnik Voronezhskogo Gosudarstvennogo Universiteta. Seriya: Geologiya [Proceedings of Voronezh State University. Series: Geology], 1, 94–103. DOI: https://doi.org/10.17308/geology.2021.1/3341[in Russian].
Osipov, V.I. (2001). Prirodnye katastrofy na rubezhe XXI veka. [Natural disasters at the turn of the XXI century]. Vestnik Rossijskoj Akademii nauk [Bulletin of the Russian Academy of Sciences]. M., 71 (4), 291-302 [in Russian].
Pozdeev, V.B. (1998). Ob opredelenii geoekologii. [On the definition of geoecology]. Geografiya i prirodnye resursy. [Geography and natural resources]. Novosibirsk, 1, 150-155 [in Russian].
Rustamov, S.G., Mardanov, I.E. (1986). Ob opolznevyh selyah Yugo-Vostochnogo Kavkaza [About landslide mud-flows in the South-Eastern Caucasus]. V sb.: Problemy protivoselevyh meropriyatij [In: Problems of anti-mudflow measures]. Alma-Ata, 90-94 [in Russian].
Fomenko, I.K., Pendin, V.V., Nguyen, Ch.K. (2017). Ocenka ushcherba, opasnosti i riska ot opolznevyh processov (na primere Severo-Zapadnogo V'etnama) [Assessment of damage, danger and risk from landslide processes (on the example of Northwest Vietnam)] Materialy dokladov XIII Obshcherossijskoj nauchno-prakticheskoj konfer-encii i vystavki «Perspektivy razvitiya inzhenernyh izyskanij v stroitel'stve v Rossijskoj Federacii» [Proceedings of the XIII All-Russian scientific-practical conference and exhibition "Prospects for the development of engineering surveys in construction in the Russian Federation"], 27-34 [in Russian].
Chalkova, Yu.S., Cherepanov, B.M. (2007). Opolznevye processy, ih prognozirovanie i bor'ba s nimi [Landslide processes, their forecasting and control]. Polzunovskij Vestnik [Polzunovsky Bulletin], 1–2, 80-89 [in Russian].
Shirinov, N.Sh. (1982). Morfostrukturnye osobennosti rajona Ismaillinskogo zemletryaseniya (Azerbajdzhanskaya SSR) [Morphostructural features of the area of the Ismayilli earthquake (Azerbaijan SSR)] Izv. AN Azerb. SSR, ser-iya nauk o Zemle [Proceedings of AS of Azerbaijan. SSR, Earth Sciences Series], 5, 9-13 [in Russian].
Ekologicheskij risk [Ecological risk]. Materialy Vtoroj Vseros. Konferencii [Materials of the Second All-Russian. Conferences]. Irkutsk: Publishing House of the Institute of Geography SB RAS, 2001, 262 [in Russian].
This work is licensed under a Creative Commons Attribution 4.0 International License.