Integrating stratified best-worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey).
Best–worst method
Erzurum
GIS
Landslide susceptibility
S-MCDM
Stratification
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
29
06
2023
accepted:
27
09
2023
medline:
27
11
2023
pubmed:
20
10
2023
entrez:
19
10
2023
Statut:
ppublish
Résumé
Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best-worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model's applicability, a case study was conducted for the Erzurum region, one of Turkey's landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study's robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600-2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.
Identifiants
pubmed: 37858024
doi: 10.1007/s11356-023-30200-9
pii: 10.1007/s11356-023-30200-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
113978-114000Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Ak MF, Yucesan M, Gul M (2022) Occupational health, safety and environmental risk assessment in textile production industry through a Bayesian BWM-VIKOR approach. Stoch Environ Res Risk Assess 36:629–642. https://doi.org/10.1007/s00477-021-02069-y
doi: 10.1007/s00477-021-02069-y
Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44
Alqadhi S, Mallick J, Talukdar S et al (2022) Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environ Sci Pollut Res 29:3743–3762. https://doi.org/10.1007/s11356-021-15886-z
doi: 10.1007/s11356-021-15886-z
Anadolu Kılıç NC (2021) Erzurum İli Doğa Olayları Profili ve Deprem Tehlikesi. Afet ve Risk Dergisi 4(1):61–83. https://doi.org/10.35341/afet.896845
doi: 10.35341/afet.896845
Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32(4):269–277
Asadabadi MR (2018) The stratified multi-criteria decision-making method. Knowl-Based Syst 162:115–123
Asadabadi MR, Ahmadi HB, Gupta H et al (2023) Supplier selection to support environmental sustainability: the stratified BWM TOPSIS method. Ann Oper Res 322:321–344. https://doi.org/10.1007/s10479-022-04878-y
doi: 10.1007/s10479-022-04878-y
Asadabadi MR, Zwikael O (2021) Integrating risk into estimations of project activities’ time and cost: a stratified approach. Eur J Oper Res 291(2):482–490. https://doi.org/10.1016/j.ejor.2019.11.018
Asadabadi MR, Saberi M, Chang E (2017) Logistic informatics modelling using concept of stratification (CST). In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, pp 1–7. https://doi.org/10.1109/FUZZ-IEEE.2017.8015510
Atabey E (2000) Earthquake. General directorate of mineral research and exploration. General directorate of mineral research and exploration, Ankara
Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81(4):432–445
Aydınözü D (2013) Yükseldikçe Bölgelerimize Göre Her 100 m. deki Yağiş Artişi Üzerine Bir Deneme. Marmara Coğrafya Dergisi 0(17):174–186
Çan T, Duman T, Olgun Ş, Çörekçioğlu Ş, Gülmez F, Elmacı H, Hamzaçebi S, Emre Ö (2013) Türkiye Heyelan Veri Tabanı. https://www.hkmo.org.tr/resimler/ekler/85a47f65233d5d0_ek.pdf . Accessed 20 June 2022
Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16(5):427–445
Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44(8):949–962
Chalkias C, Ferentinou M, Polykretis C (2014) GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece. Geosciences 4(3):176–190
Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833
Chen W, Zhang S (2021) GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling. CATENA 203:105344
Climate Change and Agriculture Evaluation Report (2021) https://www.tarimorman.gov.tr/TRGM/Duyuru/428/Iklim-Degisikligi-Ve-Tarim-Degerlendirme-Raporu Date of access: 25.10.2022
Corine Land Cover 2018 (CLC) (2018) https://land.copernicus.eu/pan-european/corine-land-cover Date of access: 25.10.2022
Cui P, Peng J, Shi P, Tang H, Ouyang C, Zou Q, ... Lei Y (2021) Scientific challenges of research on natural hazards and disaster risk. Geogr Sustain 2(3):216–223. https://doi.org/10.1016/j.geosus.2021.09.001
Dai F, Lee C (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38(5):911–923. https://doi.org/10.1139/t01-021
doi: 10.1139/t01-021
Dai FC, Lee CF, Li JXZW, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391. https://doi.org/10.1007/s002540000163
Demir G (2019) GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey). CATENA 183:104211
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling- Narayanghat road section in Nepal Himalaya. Nat Hazards 65(1):135–165
Disaster and Emergency Management Presidency (AFAD) (2022) https://www.afad.gov.tr/ Date of access: 25.10.2022
Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75(3–4):229–250
Erinç S (1953) Geography of Eastern Anatolia. Faculty of Literature Geography Institute Publications no:15, Istanbul
General Directorate of Highways (KGM) (2022). https://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Root/default.aspx Date of access: 25.10.2022
General Directorate of Mineral Research and Exploration (MTA) (2022) https://www.mta.gov.tr/ Date of access: 25.10.2022
Gigović L, Drobnjak S, Pamučar D (2019) The application of the hybrid GIS spatial multi-criteria decision analysis best–worst methodology for landslide susceptibility mapping. ISPRS Int J Geo Inf 8(2):79
Gönültaş H, Kızılaslan H, Kızılaslan N (2020) Projections of effects of global warming on rainfall regime in some provinces; Ankara, Rize, Aydın and Hakkâri provinces example. Turk J Agric -Food Sci Technol 8(10):2156–2163
Gopinath G, Jesiya N, Achu AL et al (2023) Ensemble of fuzzy-analytical hierarchy process in landslide susceptibility modeling from a humid tropical region of Western Ghats, Southern India. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-27377-4
doi: 10.1007/s11356-023-27377-4
Grozavu A, Pleşcan S, Patriche CV, Mărgărint MC, Roşca B (2013) Landslide susceptibility assessment: GIS application to a complex mountainous environment. The carpathians: integrating nature and society towards sustainability. Springer, Berlin Heidelberg, pp 31–44
Gul M, Ak MF (2020) Assessment of occupational risks from human health and environmental perspectives: a new integrated approach and its application using fuzzy BWM and fuzzy MAIRCA. Stoch Environ Res Risk Assess 34:1231–1262. https://doi.org/10.1007/s00477-020-01816-x
doi: 10.1007/s00477-020-01816-x
Hasekiogulları GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179
He S, Pan P, Dai L, Wang H, Liu J (2012) Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology 171:30–41
Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118. https://doi.org/10.1016/j.geomorph.2016.02.012
doi: 10.1016/j.geomorph.2016.02.012
Hong H, Naghibi SA, Dashtpagerdi MM, Pourghasemi HR, Chen WA (2017) Comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci 10:167
Kang D, Manirathinam T, Geetha S, Narayanamoorthy S, Ferrara M, Ahmadian A (2023) An advanced stratified decision-making strategy to explore viable plastic waste-to-energy method: a step towards sustainable dumped wastes management. Appl Soft Comput 143. https://doi.org/10.1016/j.asoc.2023.110452
Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408
Li Y, Chen W (2020) Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water 12:113. https://doi.org/10.3390/w12010113
doi: 10.3390/w12010113
Li R, Wang N (2019) Landslide susceptibility mapping for the Muchuan County (China): a comparison between bivariate statistical models (WoE, EBF, and IoE) and their ensembles with logistic regression. Symmetry 11(6):762. https://doi.org/10.3390/sym11060762
doi: 10.3390/sym11060762
Liang F, Brunelli M, Rezaei J (2020) Consistency issues in the best worst method: measurements and thresholds. Omega 96:102175
Listo FDLR, Vieira BC (2012) Mapping of risk and susceptibility of shallow landslide in the city of São Paulo, Brazil. Geomorphology 169:30–44
Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Proc Land 29(6):687–711
Meteorological Service (MGM) (2022) https://www.mgm.gov.tr/ Date of access: 25.10.2022
Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola watershed, Darjiling: a remote sensing and GIS basedanalytical hierarchy process (AHP). J Indian Soc Remote Sens 40(3):483–496
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30
Moore ID, Gessler PE, Nielsen GAE, Peterson GA (1993) Soil attribute prediction using terrain analysis. Soil Sci Soc Am J 57(2):443–452
Moreno-Solaz H, Artacho-Ramírez M, Aragonés-Beltrán P, Cloquell-Ballester V (2023) Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method. Heliyon 9(4). https://doi.org/10.1016/j.heliyon.2023.e15481
Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197
Özşahin E (2014) Tekirdağ ilinde coğrafi bilgi sistemleri ve analitik hiyerarşi süreci kullanarak heyelan duyarlılık analizi. HUMANITAS-Uluslararası Sosyal Bilimler Dergisi 2(3):167–186
Pareek N, Sharma ML, Arora MK (2010) Impact of seismic factors on landslide susceptibility zonation: a case study in part of Indian Himalayas. Landslides 7(2):191–201
Pham BT, Prakash I, Singh SK, Shirzadi A, Shahabi H, Bui DT (2019) Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. CATENA 175:203–218
Phong TV, Phan TT, Prakash I, Singh SK, Shirzadi A, Chapi K, ... Pham BT (2019) Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto Int 36(15):1685–1708. https://doi.org/10.1080/10106049.2019.1665715
Phukon P, Chetia D, Das P (2012) Landslide susceptibility assessment in the Guwahati city, Assam using analytic hierarchy process (AHP) and geographic information system (GIS). Int J Comput Appl Eng Sci 2:1–6
Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw 25:747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian J Geosci 3(3):319–326
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57
Rezaei J (2020) A concentration ratio for nonlinear best worst method. Int J Inf Technol Decis Mak 19(03):891–907
Rezaei J, Nispeling T, Sarkis J, Tavasszy L (2016) A supplier selection life cy-cle approach integrating traditional and environmental criteria using the best worst method. J Clean Prod 135:577–588
Ruff M, Czurda K (2008) Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria). Geomorphology 94(3–4):314–324
Selvaraj G, JeongHwan J (2022) Decision-making technique to achieve stratified target performance: analyze science and technology innovation policy investment of South Korea. Int J Intell Syst 37:4670–4714. https://doi.org/10.1002/int.22736
doi: 10.1002/int.22736
Sharma LP, Patel N, Debnath P, Ghose MK (2012) Assessing landslide vulnerability from soil characteristics—a GIS-based analysis. Arab J Geosci 5(4):789–796
Sun X, Chen J, Bao Y, Han X, Zhan J, Peng W (2018) Landslide susceptibility mapping using logistic regression analysis along the Jinsha river and its tributaries close to Derong and Deqin County, Southwestern China. ISPRS Int J Geo-Inf 7:438. https://doi.org/10.3390/ijgi7110438
doi: 10.3390/ijgi7110438
Torkayesh EA, Malmir B, Asadabadi RM (2021) Sustainable waste disposal technology selection: the stratified best-worst multi-criteria decision-making method. Waste Manag 122:100–112. https://doi.org/10.1016/j.wasman.2020.12.040
doi: 10.1016/j.wasman.2020.12.040
Torkayesh EA, Yazdani M, Ribeiro-Soriano D (2022) Analysis of industry 4.0 implementation in mobility sector: an integrated approach based on QFD, BWM, and stratified combined compromise solution under fuzzy environment. J Ind Inform Integr 30. https://doi.org/10.1016/j.jii.2022.100406
Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides 13:305–320
Tunusluoglu MC, Gokceoglu C, Nefeslioglu HA, Sonmez H (2008) Extraction of potential debris source areas by logistic regression technique: a case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey). Environ Geol 54(1):9–22
United States Geological Survey (USGS) (2022) https://www.usgs.gov/ Date of access: 25.10.2022
URL-1 (n.d.) http://www.koeri.boun.edu.tr/ Date of access: 25.10.2022
URL-2 (n.d.) https://www.erzurum.bel.tr/ Date of access: 25.10.2022
URL-3 (n.d.) http://yerbilimleri.mta.gov.tr/anasayfa.aspx Date of access: 25.10.2022
URL-4 (n.d.) https://www.tuik.gov.tr/ Date of access: 25.10.2022
URL-5 (n.d.) https://tad.tarim.gov.tr/ Date of access: 25.10.2022
URL-6 (n.d.) http://download.geofabrik.de/europe.html Date of access: 25.10.2022
Vafadarnikjoo A, Chalvatzis K, Botelho T, Bamford D (2023) A stratified decision-making model for long-term planning: application in flood risk management in Scotland. Omega 116. https://doi.org/10.1016/j.omega.2022.102803
Wu CH, Chen SC (2009) Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method. Geomorphology 112(3):190–204
Wu Y, Li W, Liu P, Bai H, Wang Q, He J, Sun S (2016) Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environ Earth Sci 75(5):1–11. https://doi.org/10.1007/s12665-015-5194-9
Xu C, Xu X, Dai F, Saraf AK (2012) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Comput Geosci 46:317–329
Yalcin A (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
Yalcin A, Reis S, Aydinoglu AC, 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
Yong C, Jinlong D, Fei G, Bin T, Tao Z, Hao F, ... Qinghua Z (2022) Review of landslide susceptibility assessment based on knowledge mapping. Stoch Environ Res Risk Assess 1–19. https://doi.org/10.1007/s00477-021-02165-z
Zadeh LA (2016) Stratification, target set reachability and incremental enlargement principle. Inf Sci 354:131–139
Zhang L, Arabameri A, Santosh M et al (2023) Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-27799-0
doi: 10.1007/s11356-023-27799-0
Zhao X, Chen W (2020) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12(14):2180