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Landside sensitivity model creation based on SMCA-GIS with verification of point coordinate variation

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Abstract

The primary goal of the present study was to use Spatial Multi-Criterion Analysis (SMCA) management based on the integration of Analytical Hierarchical Process (AHP) and Geographic Information System (GIS) approaches to construct a landslide susceptibility map** model for the Central Black Sea region. Four different main indicators (topography, land use-land cover, geology, and soil) were determined in the model, and maps related to them were produced using GIS. According to the present model, 10.0% of the research area was found to be in the high-risk class, whereas 30.3% were in the low and very low-risk classes. On the other hand, the medium-risk class covered more than half of the study area. Moreover, measurements were taken on a control network consisting of fifteen points, and mobility was observed to verify the model in the study area. For this purpose, the coordinates obtained from the measurements made in six different periods were compared, and the results revealed that the coordinate difference values were agreed with the model data. Finally, the landslide susceptibility map** model showed parallel with field validation. The current study is a guide for reducing the impact of natural disasters by monitoring landslide areas.

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References

  • Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility map** using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10(8):194. https://doi.org/10.1007/s12517-017-2980-6

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Env 58(1):21–43. https://doi.org/10.1007/s100640050066

    Article  Google Scholar 

  • Anbalagan R (1992) Landslide hazard evaluation and zonation map** in mountainous terrain. Eng Geol 32:269–277

    Article  Google Scholar 

  • Arca D, Keskin Citiroglu H, Tasoglu IK (2019) A comparison of GIS-based landslide susceptibility assessment of the Satuk village (Yenice, NW Turkey) by frequency ratio and multi-criteria decision methods. Environ Earth Sci 78:81. https://doi.org/10.1007/s12665-019-8094-6

    Article  Google Scholar 

  • Bitelli G, Dubbini M, Zanutta A (2004) Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies. International Archives of Photogrammetry. Remote Sens Spatial Inform Sci 35:246–251

    Google Scholar 

  • Coe JA, Ellis WL, Godt JW, Savage WZ, Savage JE, Michael JA, Debray S (2003) Seasonal movement of the Slumgullion landslide determined from Global Positioning System surveys and field instrumentation, July 1998–March 2002. Eng Geol 68(1–2):67–101

    Article  Google Scholar 

  • Choubey VD, Litoria PK (1990) Terrain classification and land hazard map** in Kalsi-Chakrata area (Garhwal Himalaya), India. ITC J 1:65–68

    Google Scholar 

  • Çevik E, Topal T (2003) GIS-based landslide susceptibility map** for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44(8):949–962

    Article  Google Scholar 

  • Dengiz O, Gülser C, İç S, ve Kara Z (2009) Physical and chemical properties and map** of the soils of the Aşağıaksu basin. Anadolu J Agric Sci 24(1):33–43

  • Du J, Yin K, Lacasse S (2013) Displacement prediction in colluvial landslides, three Gorges reservoir. China Landslides 10(2):203–218

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C, Van Asch THWJ (2004) Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23

    Article  Google Scholar 

  • Erener A, Düzgün HSB (2010) Improvement of statistical landslide susceptibility map** by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7(1):55–68

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T, Rafiq L (2011) GIS-Based Landslide Susceptibility Map**: A Case Study in Bostan Abad County, Iran. In: International conference Geoinformatics for Disaster Management, pp 03–08

  • Fernandez CI, Del Castillo TF, El Hamdouni R, Montero JC (1999) Verification of landslide susceptibility map**: a case study. Earth Surf Proc Landf 24:537–544

    Article  Google Scholar 

  • Gabriel KR (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58(3):453–467

    Article  Google Scholar 

  • Gauch HG, Zobel RW (1996) AMMI analyses of yield trials. In Kang MS and Gauch HG (eds). Genotype by Environment Interaction. CRC. Boca Raton, Florida, pp 85–122

  • Gili JA, Corominas J, Rius J (2000) Using Global Positioning System techniques in landslide monitoring. Eng Geol 55(3):167–192

    Article  Google Scholar 

  • Ginanjar I, Pasaribu US, Indratno SW (2017) A measure for objects clustering in principal component analysis biplot: a case study in inter-city buses maintenance cost data. AIP Conf Proc 1827(1):020016-1–020016-7

    Google Scholar 

  • Gougazeh M, Al-Shabatat A (2013) Geological and geotechnical properties of soil materials at Tannur dam, Wadi Al Hasa, South Jordan. J Taibah Univr Sci 7(4):216–224

    Article  Google Scholar 

  • Gökçeoğlu C, Ercanoğlu M (2001) Heyelan duyarlılık haritalarının hazırlanmasında kullanılan parametrelere ilişkin belirsizlikler. Yerbilimleri 22(23):189–206

    Google Scholar 

  • Gökçeoğlu C, Sönmez H, Nefeslioğlu HA, Duman TY, Can T (2005) The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity. Eng Geol 81(1):65–83

    Article  Google Scholar 

  • Guzzetti F, Carrarra A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:81–216

    Article  Google Scholar 

  • Gürsoy FE, Dengiz O (2018) Morphology, minerology properties and classification of vertisols formed on two different parent material. Anadolu J Agric Sci 33(2):162–169

    Article  Google Scholar 

  • Hadmoko DS, Lavigne F, Sartohadi J (2010) Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia. Nat Hazards 54:623–642. https://doi.org/10.1007/s11069-009-9490-0

    Article  Google Scholar 

  • Hervás J, Barredo JI, Rosin PL, Pasuto A, Mantovani F, Silvano S (2003) Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy. Geomorphology 54(2):63–75

    Article  Google Scholar 

  • Javadi P (2017) Detecting the unstable points in deformation monitoring geodetic networks in analysis method of subnetwork. Mod Appl Sci 11(3):61–75

    Article  Google Scholar 

  • Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility map** for the 2005 Kashmir earthquake region. Geomorphology 101(4):631–642

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility map**: a case study from the Tinau Watershed, West Nepal. Comput Geosci 52:398–408

    Article  Google Scholar 

  • Khadka A, Dhakal S, Budha P (2018) GIS based landslide susceptibility map** along the road section from Bandeu to Barahabise, Sindhupal Chowk District of Nepal. Int J Sci Res 7(11):466–471

    Google Scholar 

  • Kitutu GM, Muwanga A, Poesen J, Deckers J (2009) Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda. Afr J Agric Res 4(7):611–620

    Google Scholar 

  • Knapen A, Kitutu MG, Poesen J, Breugelmans W, Deckers J, Muwanga A (2006) Landslides in a densely populated county at the foot-slopes of Mount Elgon (Uganda): characteristics and causal factors. Geomorphology 73:149–165

    Article  Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in Perialpine Slovenia. Geomorphology 74(1):17–28

    Article  Google Scholar 

  • Kosko B (1986) Fuzzy entropy and conditioning. Inf Sci 40:165–174

    Article  Google Scholar 

  • Koukis G, Ziourkas C (1991) Slope instability phenomena in Greece: a statistical anlysis. Bull Int Assoc Eng Geol 43:47–60

    Article  Google Scholar 

  • Kumtepe P, Nurlu Y, Cengiz T, Sütçü E (2009) Bolu Çevresinin Heyelan Duyarlılık Analizi, TMMOB Coğrafi Bilgi Sistemleri Kongresi, 02–06 Kasım, İzmir (in Turkish)

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113

    Article  Google Scholar 

  • Malet JP, Maquaire O, Calais E (2002) The use of global positioning system techniques for the continuous monitoring of landslides: application to the super-sauze earthflow (Alpes-de-Haute-Provence, France). Geomorphology 43(2):33–54

    Article  Google Scholar 

  • McColl ST, McCabe M (2016) The causes and agricultural impacts of large translational landslides: Case-studies from North Island, New Zealand. Landslides and Engineered Slopes. Experience, Theory and Practice, Proceedings of the 12th International Symposium on Landslides (Napoli, Italy, 12–19 June 2016) (1st ed.). CRC Press, pp 1401–1408

  • Mehrotra GS, Sarkar S, Dharmaraju R (1992) Landslide hazard assessment in Rishikesh-Tehri area, Garhwal Himalaya, India. In: Proceedings of 6th International Symposium on the Landslides, 10–14 February 1992, Christchurch, England, Bell DH (ed), Balkema, pp 1001–1007

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility map** at Golestan Province, Iran: a comparison between frequency ratio, dempster-shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236

    Article  Google Scholar 

  • Nagarajan R, Roy A, Vinod Kumar R, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility map** based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Env 58:275–287

    Article  Google Scholar 

  • Öner E (1996) Samsun ve çevresinin jeomorfolojisi. Coğrafya Araştırmaları Dergisi 4:191–222

    Google Scholar 

  • Özkan B, Dengiz O, Turan İD (2020) Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem. Sci Rep 10(1):1–18

    Article  Google Scholar 

  • Papathanassiou G, Valkaniotis S, Ganas A, Pavlides S (2013) GIS-based statistical analysis of the spatial distribution of earthquake-induced landslides in the İsland of Lefkada, Ionian Islands. Greece Landslides 10(6):771–783

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility map** using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje Area. Korea Environ Earth Sci 68(5):1443–1464

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility map** using index of entropy and conditional probability models in Gis: Safarood Basin, İran. CATENA 97:71–84

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network model. Environ Model Softw 25:747–759

    Article  Google Scholar 

  • Psomiadis E, Papazachariou A, Soulisi KX, Alexiou DS, Charalampopoulos I (2020) Landslide map** and susceptibility assessment using geospatial analysis and earth observation data. Land 9(5):133–159. https://doi.org/10.3390/land9050133

    Article  Google Scholar 

  • Rifkhatussa EF, Yasin H, Rusgiyono A (2014) Analisis Biplot Komponen Utama pada Bank Umum (Commercial Bank) yang Beroperasi di Jawa Tengah, Jurnal Gaussian, pp 61–70

  • Saaty T (1980) The analytical hierarchy process. McGraw Hill, New York

    Google Scholar 

  • Sadeghi SM, Samizadeh H, Amiri E, Ashouri M (2011) Additive main effects and multiplicative interactions (AMMI) analysis of dry leaf yield in tobacco hybrids across environments. Afr J Biotech 10:4358–4364

    Google Scholar 

  • Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens 23:357–369

    Article  Google Scholar 

  • Schuster RL, Highland L (2007) The Third Hans Cloos Lecture. Urban landslides: socioeconomic impacts and overview of mitigative strategies. Bull Eng Geol Env 1:1–27. https://doi.org/10.1007/s10064-006-0080-z

    Article  Google Scholar 

  • Shahabi H, Hashim M, Ahmad BB (2015) Remote sensing and GIS-based landslide susceptibility map** using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin. Iran Environ Earth Sci 73:8647–8668

    Article  Google Scholar 

  • Soil Survey Staff (2014) Keys to Soil Taxonomy. United States Department of Agriculture Natural Resources Conservation Service Washington D.C, USA

    Google Scholar 

  • Tanır E (2000) Deformasyon analizinde statik değerlendirme yöntemleri. MSc Thesis Karadeniz Teknik Üniversitesi Fen Bilimleri Enstitüsü Jeodezi ve Fotogrametri Mühendisliği Ana Bilim Dalı, 104, Trabzon (in Turkish)

  • Turan M, Dengiz O, Turan Demirağ İ (2018) Determination of soil moisture and temperature regimes for Samsun Province According to Newhall model. Türkiye Tarımsal Araştırmalar Dergisi 5:131–142

    Article  Google Scholar 

  • Turan İD, Özkan B, Türkeş M, Dengiz O (2020) Landslide susceptibility map** for the Black Sea Region with spatial fuzzy multi-criteria decision analysis under semi-humid and humid terrestrial ecosystems. Theoret Appl Climatol 140:1233–1246

    Article  Google Scholar 

  • Uromeihy A, Mahdavifar MR (2000) Landslide hazard zonation of the Khorshrostam area. Iran Bull Eng Geol Environ 58:207–213

    Article  Google Scholar 

  • Vijith H, Madhu G (2008) Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environ Geol 55:1397–1405

    Article  Google Scholar 

  • Wu Y, Li W, Liu P, Bai H, Wang Q, He J, Liu Y, Sun S (2016) Application of analytic hierarchy process model for landslide susceptibility map** in the Gangu County, Gansu Province, China. Environ Earth Sci 75:422. https://doi.org/10.1007/s12665-015-5194-9

    Article  Google Scholar 

  • Yalçın A (2007) Heyelan Duyarlılık Haritalarının Üretilmesinde Analitik Hiyerarşi Yönteminin ve CBS’nin Kullanımı. Selçuk Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 23:1–14

    Google Scholar 

  • Yalçın A (2008) GIS-based landslide susceptibility map** using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA 72:1–12

    Article  Google Scholar 

  • Yalçınkaya M, Teke K, Bayrak T (2003) Jeodezik GPS ağlarında duyarlık ve güven analizi. I. Ulusal Mühendislik Ölçmeleri Sempozyumu, 30–31 Ekim, İstanbul (in Turkish)

  • Yan F, Zhang Q, Ye S, Ren B (2019) A novel hybrid approach for landslide susceptibilitymap** integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model. Geomorphology 327:170–187

    Article  Google Scholar 

  • Yan W (2001) GGEbiplot—a Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron J 93:1111–1118

    Article  Google Scholar 

  • Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86:623–645

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility map** using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Zobel RW, Wright MJ, Gauch HG Jr (1988) Statistical analysis of a yield trial. Agron J 80:388–393

    Article  Google Scholar 

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Acknowledgements

We would like to thank late dear Nihat KARAAHMETOĞLU, who helped with the field and laboratory studies. Also, We would like to thank dear Assist. Prof. Muhammad Azhar NADEEM from Sivas University Science and Technology for editing the manuscript. In addition, this study was produced from PhD thesis of Fikret SAYGIN and supported by Ondokuz Mayis University coded PYO.ZRT.1901.13.001 project number.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Faculty of Agriculture Sciences and Technology, Plant Production and Technology Department, Sivas University of Science and Technology, Sivas, Turkey

    F. Saygın

  2. Agricultural Faculty, Plant Nutrition and Soil Science Department, Ondokuz Mayıs University, Samsun, Turkey

    O. Dengiz

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  1. F. Saygın
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Contributions

FS: Performed the experiments, Data curation, Investigation, Validation, Writing-Original draft preparation. OD: Supervision, Conceptualization, Methodology, Reviewing and Editing.

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Correspondence to O. Dengiz.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Financial interests: All authors declare that they have no financial interests. The authors declare that they have no conflict of interest related to the content of this manuscript. The authors have no relevant financial or non-financial interests to disclose.

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We hereby would like to warrant that the manuscript represents original work that is not being considered for publication, in whole or in part, in another journal, book, conference proceedings, or government publication with a substantial circulation. I would like to warrant that all previously published work cited in the manuscript has been fully acknowledged. In addition, our manuscript has not been submitted to a preprint server prior to submission on ESPR.

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Editorial responsibility: S Mirkia.

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Saygın, F., Dengiz, O. Landside sensitivity model creation based on SMCA-GIS with verification of point coordinate variation. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05766-7

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