Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility assessment using triangular fuzzy number-analytic hierarchy processing (TFN-AHP), contributing weight (CW) and random forest weighted frequency ratio (RF weighted FR) at the Pengyang county, Northwest China

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

A Correction to this article was published on 02 March 2022

This article has been updated

Abstract

Landslides in the loess area of China pose a great threat to the lives of local residents. Therefore, it is necessary to study the landslide susceptibility in this region. This paper takes Pengyang County in northwest China as the research area, selects 12 influencing factors [including elevation, slope, slope aspect, plan curvature, profile curvature, distance to rivers, groundwater types, land use, normalized difference vegetation index (NDVI), rainfall, lithology and peak ground acceleration(PGA)], and uses triangular fuzzy number-analytic hierarchy process (TFN-AHP), contributing weight (CW) and random forest weighted frequency ratio (RF weighted FR) to carry out the landslide susceptibility. The weight of landslide influencing factors and classified influencing factors calculated by TFN-AHP, CW and RF weighted FR are ranked. The AUC values of the landslide susceptibility assessment results of TFN-AHP, CW and RF weighted FR are all above 0.70, and the prediction effect is good. In addition, the new landslides found in the three field investigations is consistent with the superposition of high union very high landslide susceptibility areas, indicating that the landslide susceptibility assessment results made in this paper are reliable. The research results of this paper are helpful to explore the relationship between landslide and influencing factors in loess area, which can be used as a reference for the susceptibility assessment in loess area of China, and can provide a basis for disaster prevention and reduction for government departments in Southern Ningxia of China.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Change history

References

  • Abedini M, Tulabi S (2018) Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran. Environ Earth Sci 77:405. https://doi.org/10.1007/s12665-018-7524-1

    Article  Google Scholar 

  • Achour Y, Saidani Z, Touati R, Pham QB, Pal SC, Mustafa F, Sanli FB (2021) Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality. Environ Earth Sci 80(17):575. https://doi.org/10.1007/s12665-021-09889-9

    Article  Google Scholar 

  • Ahlgren P, Jarneving B, Rousseau R (2003) Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. J Am Soc Inform Sci Technol 54(6):550–560. https://doi.org/10.1002/asi.10242

    Article  Google Scholar 

  • Ahmed F, Kilic K (2019) Fuzzy analytic hierarchy process: a performance analysis of various algorithms. Fuzzy Sets Syst 362:110–128. https://doi.org/10.1016/j.fss.2018.08.009

    Article  Google Scholar 

  • Ayağ Z, Özdemir RG (2006) A fuzzy AHP approach to evaluating machine tool alternatives. J Intell Manuf 17(2):179–190. https://doi.org/10.1007/s10845-005-6635-1

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Chang D (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655. https://doi.org/10.1016/0377-2217(95)00300-2

    Article  Google Scholar 

  • Chen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. CATENA 195:104777. https://doi.org/10.1016/j.catena.2020.104777

    Article  Google Scholar 

  • Chen W, Ding X, Zhao R, Shi S (2016) Application of frequency ratio and weights of evidence models in landslide susceptibility mapping for the Shangzhou District of Shangluo City, China. Environ Earth Sci 75(1):64. https://doi.org/10.1007/s12665-015-4829-1

    Article  Google Scholar 

  • Chen G, Zhang X, Xue MJ et al (2019) Digital terrain modeling and geological analysis. Southeast University Press, Nanjing (in Chinese)

    Google Scholar 

  • China Geological Survey (2008) DD 2008-02 Code for investigation of landslide, collapse and debris flow (1:50000) (in Chinese)

  • Chowdhuri I, Pal SC, Arabameri A, Ngo PTT, Chakrabortty R, Malik S, Das D, Roy P (2020) Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci 79(20):476. https://doi.org/10.1007/s12665-020-09227-5

    Article  Google Scholar 

  • Dehnavi A, Aghdam IN, Pradhan B, Varzandeh MHM (2015) A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA 135:122–148. https://doi.org/10.1016/j.catena.2015.07.020

    Article  Google Scholar 

  • Deng R, Ren B, Zhou S (2010) Selection of technology for small sewage treatment based on analytic hierarchy process (AHP) with triangular fuzzy numbers. Environ Eng 28(1):91–92 (in Chinese)

    Google Scholar 

  • Deng X, Li J, Zeng H, Chen J, Zhao J (2012) Research on computation methods of AHP wight vector and its applications. Math Practice Theory 42(7):93–100 (in Chinese)

    Google Scholar 

  • Deng N, Cui Y, Guo Y (2020) Frequency ratio-random forest-model-based landslide susceptibility. Assess Sci Technol Eng 20(34):13990–13996 (in Chinese)

    Google Scholar 

  • Department of Geology and geography, Lanzhou University, Zhang XG (1983) Engineering geology. Geological Publishing House, Beijing (in Chinese)

    Google Scholar 

  • Depicker A, Jacobs L, Delvaux D, Havenith HB, Mateso JCM, Govers G, Dewitte O (2020) The added value of a regional landslide susceptibility assessment: the western branch of the East African Rift. Geomorphology 353:106886. https://doi.org/10.1016/j.geomorph.2019.106886

    Article  Google Scholar 

  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen ZC, Han Z, Pham BT (2019) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed. Japan Landslides 17(3):641–658. https://doi.org/10.1007/s10346-5

    Article  Google Scholar 

  • Fan M, Yuan X, Zhu H, Huang H, Zeng G, Liang Y, Jiang H (2010) Assessment model for heavy metal pollution in river sediment based on triangular fuzzy numbers. Acta Sci Circum 30(8):1700–1706 (in Chinese)

    Google Scholar 

  • Fan L, Hu R, Zeng F, Wang S, Zhang X (2012) Application of weighted information value model to landslide susceptibility assessment-a case study of Enshi city, Hubei province. J Eng Geol 20(4):508–513 (in Chinese)

    Google Scholar 

  • Fan X, Scaringi G, Korup O, Joshua West A, van Westen CJ, Tanyas H, Hovius N, Hales TC, Jibson RW, Allstadt KE, Zhang L, Evans SG, Xu C, Li G, Pei X, Xu Q, Huang R (2019) Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57(2):421–503. https://doi.org/10.1029/2018RG000626

    Article  Google Scholar 

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3–4):85–98. https://doi.org/10.1016/j.enggeo.2008.03.022

    Article  Google Scholar 

  • Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Learn Res 15(1):3133–3181. http://dl.acm.org/citation.cfm?id=2697065. Accessed 22 Jan 2022

    Google Scholar 

  • Fu W (2005) Lability zoning study and countermeasure of geological hazard—taking Nanjing as an example. Dissertation, China University of Geosciences (in Chinese)

  • Garcia-Chevesich P, Wei X, Ticona J, Martínez G, Zea J, García V, Alejo F, Zhang Y, Flamme H, Graber A, Santi P, McCray J, Gonzáles E, Krahenbuhl R (2021) The impact of agricultural irrigation on landslide triggering: a review from Chinese, English, and Spanish Literature. Water 13(1):10. https://doi.org/10.3390/w13010010

    Article  Google Scholar 

  • Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 221:227–252. https://doi.org/10.1016/j.earscirev.2016.08.011

    Article  Google Scholar 

  • Gong P, Chen B, Li X et al (2020) Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Sci Bull 65(3):182–187. https://doi.org/10.1016/j.scib.2019.12.007

    Article  Google Scholar 

  • Guo Z, Yin K, Fu S, Huang F, Gui L, Xia H (2019) Evaluation of landslide susceptibility based on GIS and WOE-BP model. Earth Sci 44(12):4299–4312 (in Chinese)

    Google Scholar 

  • Hong H, Tsangaratos P, Ilia I, Loupasakis C, Wang Y (2020a) Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping. Sci Total Environ 742:140549. https://doi.org/10.1016/j.scitotenv.2020.140549

    Article  Google Scholar 

  • Hong H, Liu J, Zhu AX (2020b) Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci Total Environ 718:137231. https://doi.org/10.1016/j.scitotenv.2020.137231

    Article  Google Scholar 

  • Hu X, Mei H, Zhang H, Li Y, Li M (2020) Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China. Nat Hazards 105(2):1663–1689. https://doi.org/10.1007/s11069-020-04371-4

    Article  Google Scholar 

  • Hu X, Huang C, Mei H, Zhang H (2021) Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges Reservoir Area, China. Bull Eng Geol Environ 80(7):5315–5329. https://doi.org/10.1007/s12665-017-6731-5

    Article  Google Scholar 

  • Huang R (2007) Large-scale landslides and their sliding mechanisms in China since the 20th century. Chin J Rock Mech Eng 26(3):433–454 (in Chinese)

    Google Scholar 

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926. https://doi.org/10.1007/s13762-013-0464-0

    Article  Google Scholar 

  • Jaafari A, Rezaeian J, Omrani MS (2017) Spatial prediction of slope failures in support of forestry operations safety. Croatian Journal of Forest Engineering: J Theory Appl For Eng 38:107–118. https://hrcak.srce.hr/174455. Accessed 20 Jan 2022

    Google Scholar 

  • Jiang S (2020) DEM based Research on Terrain Texture of The Loess Plateau. Dissertation, Nanjing Normal University (in Chinese)

  • Kavzoglu T, Colkesen I, Sahin EK (2019) Machine learning techniques in landslide susceptibility mapping: a survey and a case study. Machine learning techniques in landslide susceptibility mapping: a Survey and a Case Study. In: Pradhan S, Vishal V, Singh T (eds) Landslides: theory, practice and modelling. Advances in natural and technological hazards research, vol 50. Springer, Cham

    Google Scholar 

  • Lan H, Wu FQ, Zhou C, Wang S (2002) Analysis on susceptibility of GIS based landslide triggering factors in Yunnan Xiaojiang watershed. Chin J Rock Mech Eng 21(10):1500–1506 (in Chinese)

    Google Scholar 

  • Lee S, Baek WK, Jung HS, Lee S (2020) Susceptibility mapping on urban landslides using deep learning approaches in Mt. Umyeon. Appl Sci 10(22):8189. https://doi.org/10.3390/app10228189

    Article  Google Scholar 

  • Li W, Wang X (2020) Application and comparison of frequency ratio and information value model for evaluating landslide susceptibility of loess gully region. J Nat Disasters 29(4):213–220 (in Chinese)

    Google Scholar 

  • Liang Z, Wang C, Khan KUJ (2020) Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping. Stoch Environ Res Risk Assess 35(6):1243–1256. https://doi.org/10.1007/s00477-020-01893-y

    Article  Google Scholar 

  • Lin X, Guo Y (2001) A study on coupling relation between landslide and rainfall. J Catastrophol 16(2):87–92 (in Chinese)

    Google Scholar 

  • Lin F, Liu J, Xu S, Liu M, Zhang M, Liang S (2020) Evaluation method of landslide susceptibility based on random forest weighted information. Sci Surv Mapping 45(12):131–138

    Google Scholar 

  • Long J, Liu Y, Li C, Fu J, Zhang H (2020) A novel model for regional susceptibility mapping of rainfall-reservoir induced landslides in Jurassic slide-prone strata of western Hubei Province, Three Gorges Reservoir area. Stoch Environ Res Risk Assess 37(7):1403–1426. https://doi.org/10.1007/s00477-020-01892-z

    Article  Google Scholar 

  • Lyu HM, Sun WJ, Shen SL, Zhou AN (2020) Risk assessment using a new consulting process in fuzzy AHP. Constr Eng Manag 56:102103. https://doi.org/10.1016/j.scs.2020.102103

    Article  Google Scholar 

  • Ma S, Qiu H, Pei Y, Yang W, Yang D, Cao M (2020) Quantitative assessment of landslide susceptibility on the Loess Plateau in China. Phys Geogr 41:489–516. https://doi.org/10.1080/02723646.2019.1674559

    Article  Google Scholar 

  • Mallick J, Singh RK, AlAwadh MA, Islam S, Khan RA, Qureshi MN (2018) GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia. Environ Earth Sci 77:276. https://doi.org/10.1007/s12665-018-7451-1

    Article  Google Scholar 

  • Mao Z (2009) Research on Geological Hazard Assessment in Pengyang County, Ningxia. Dissertation, Chang'an University (in Chinese)

  • Mao Z, Zhang J, Xue X, Yu B, Lai H, Cui Z, Tian Y (2017) Analysis of factors causing water damage to loess double-arched tunnel based on TFN-AHP. Civil Engg J 26(1):51–65. https://doi.org/10.14311/CEJ.2017.01.0006

    Article  Google Scholar 

  • Mao Z, Wang S, An N (2020) Research on slope ecological restoration and management in mining areas on the loess plateau: planting support technology of PVC plates lattice anchor beam. Coal Sci Technol 48(4):121–128

    Google Scholar 

  • Mondal S, Mandal S (2020) Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India. Geocarto Int 35(8):818–856. https://doi.org/10.1080/10106049.2018.1544288

    Article  Google Scholar 

  • Moragues S, Lenzano MG, Lanfri M, Moreiras M, Lannutti E, Lenzano L (2020) Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake, Argentina. Nat Hazards 105(1):915–941. https://doi.org/10.1007/s11069-020-04343-8

    Article  Google Scholar 

  • Nhu VH, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Clague JJ, Jaafari A, Chen W, Nguyen H (2020) Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. Int J Environ Res Public Health 17(4):4933. https://doi.org/10.3390/ijerph17144933

    Article  Google Scholar 

  • Orhan O, Bilgilioglu SS, Kaya Z, Ozcan AK, Bilgilioglu H (2020) Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto Int. https://doi.org/10.1080/10106049.2020.1837258

    Article  Google Scholar 

  • Panahi M, Gayen A, Pourghasemi HR, Rezaie F, Lee S (2020) Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Sci Total Environ 741:139937. https://doi.org/10.1016/j.scitotenvol2020.139937

    Article  Google Scholar 

  • Paryani S, Neshat A, Javadi S, Pradhan B (2020) Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping. Nat Hazards 103(2):1961–1988. https://doi.org/10.1007/s11069-020-04067-9

    Article  Google Scholar 

  • Peng J, Wang S, Wang Q, Zhuang J, Huang W, Zhu X, Leng Y, Ma P (2019) Distribution and genetic types of loess landslides in China. J Asian Earth Sci 170:329–350. https://doi.org/10.1016/j.jseaes.2018.11.015

    Article  Google Scholar 

  • Pengyang County Local Chronicles Compilation Committee (2011) Pengyang County annals. Gansu Culture Press, Lanzhou (in Chinese)

    Google Scholar 

  • Pu X, Wan L, Wang P (2021) Initiation mechanism of mudflow-like loess landslide induced by the combined effect of earthquakes and rainfall. Nat Hazards 105(3):3079–3097. https://doi.org/10.1007/s11069-020-04442-6

    Article  Google Scholar 

  • Qiao J (2010) Theory and practice of landslide risk zoning. Sichuan University Press, Chengdu (in Chinese)

    Google Scholar 

  • Qiao J, Wu C (2005) The influence of relative elevation on landslide occurrence with in Yunyang-Wushan section in the Three Gorges Reservoir region. Chin J Geol Hazard Control 16(4):20–23 (in Chinese)

    Google Scholar 

  • Qiao J, Wu C, Tian H (2004) Contribution rate research of stratum to landslide growth of Yunyang-Wushan segment in Three Gorges Reservoir region. Chin J Rock Mech Eng 23(17):2920–2924 (in Chinese)

    Google Scholar 

  • Qiao J, Wu C, Tian H (2006) Contributing rates of the slope shape toward the landslide development from Yunyang to Wushan in Three Gorges Reservoir area. J Eng Geol 14(1):18–22 (in Chinese)

    Google Scholar 

  • Qiao J, Wu C, Tian H (2007) The contributing rate of slope gradient to landslide growth from Yunyang to Wushanin Three Gorges Reservoir area. J Mt Sci 25(2):207–211 (in Chinese)

    Google Scholar 

  • Qiao J, Shi L, Wang M (2008a) Regional landslide danger assessments based on GIS and the root factor contributing weight model: a case study of the Wanzhou District, Chongqing City, China. Geol Bull China 7(11):1802–1809 (in Chinese)

    Google Scholar 

  • Qiao J, Shi L, Wang M (2008b) Landslide risk zoning based on the contributing weight stack method. Geol Bull China 27(11):1787–1794 (in Chinese)

    Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int 31(1):42–70. https://doi.org/10.1080/10106049.2015.1041559

    Article  Google Scholar 

  • Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Inf 8(4):867–883. https://doi.org/10.1007/s12145-015-0220-8

    Article  Google Scholar 

  • Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001

    Article  Google Scholar 

  • Ríos R, Ribó A, Mejía R (2016) Combining neural networks and geostatistics for landslide hazard assessment of San Salvador metropolitan area, EL Salvador. Revista de Matemática: Teoría y Aplicaciones 23(1):155–172. http://www.redalyc.org/articulo.oa?id=45343487007. Accessed 20 Jan 2022

    Google Scholar 

  • Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26. https://doi.org/10.1016/0377-2217(90)90057-I

    Article  Google Scholar 

  • Sahana M, Pham BT, Shukla M, Costache R, Thu DX, Chakrabortty R, Satyam N, Nguyen HD, Phong TV, Le HV, Pal SC, Areendran G, Imdad K, Prakash I (2020) Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Geocarto Int. https://doi.org/10.1080/10106049.2020.1837262

    Article  Google Scholar 

  • Sahin EK, Colkesen I, Acmali SS, Akgun A, Aydinoglu AC (2020) Developing comprehensive geo computation tools for landslide susceptibility mapping: LSM tool pack. Comput Geosci 144:104592. https://doi.org/10.1016/j.cageo.2020.104592

    Article  Google Scholar 

  • Saranaathan SE, Mani S, Ramesh V, Venkatesh SP (2020) Landslide susceptibility zonation mapping using bivariate statistical frequency ratio method and GIS: a case study in part of SH 37 Ghat Road, Nadugani, Panthalur Taluk, The Nilgiris. J Indian Soc Remote Sens 49(2):275–291. https://doi.org/10.1007/s12524-020-01207-3

    Article  Google Scholar 

  • Shi L, Qiao J (2009) Vulnerability evaluation on regional landslides based on GIS and contribution weight superposition approach. J Catastrophol 24(3):46–50 (in Chinese)

    Google Scholar 

  • Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform 8(1):25. https://doi.org/10.1186/1471-2105-8-25

    Article  Google Scholar 

  • Su Z, Chow JK, Tan PS, Wu J, Ho YK, Wang YH (2020) Deep convolutional neural network–based pixel-wise landslide inventory mapping. Landslides 18(4):1421–1443. https://doi.org/10.1007/s10346-020-01557-6

    Article  Google Scholar 

  • Sun JZ, Wu WJ, Tian CS et al (2011) Loess Studies III—loess environment. X’ian Map Publishing House, X’ian (in Chinese)

    Google Scholar 

  • Svetnik V, Liaw A, Tong C, Culberson C, Sheridan PR, Feuston PB (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947–1958. https://doi.org/10.1021/ci034160g

    Article  Google Scholar 

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293. https://doi.org/10.1016/j.jclepro.2020.124159

    Article  Google Scholar 

  • Talebi A, Uijlenhoet R, Troch PA (2007) Soil moisture storage and hillslope stability. Nat Hazard 7(5):523–534. https://doi.org/10.5194/nhess-7-523-2007

    Article  Google Scholar 

  • Tang Y, Feng F, Guo Z, Feng F, Guo Z, Li Z, Wang J, Sun X, Ma H, Li Y (2020) Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China). J Clean Prod 227:124195. https://doi.org/10.1016/j.jclepro.2020.124159

    Article  Google Scholar 

  • Torizin J, Wang L, Fuchs M, Tong B, Balzer D, Wan L, Kuhn D, Li A, Chen L (2018) Statistical landslide susceptibility assessment in a dynamic environment: A case study for Lanzhou City, Gansu Province, NW China. J Mt Sci 15(6):1299–1318. https://doi.org/10.1007/s11629-017-4717-0

    Article  Google Scholar 

  • Trigila A, Iadanza C, Esposito C, Scarascia-Mugnozza G (2015) Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119–136. https://doi.org/10.1016/j.geomorph.2015.06.001

    Article  Google Scholar 

  • Tsaur SH, Chang TY, Yen CH (2002) The evaluation of airline service quality by fuzzy MCDM. Tour Manag 23(2):107–115. https://doi.org/10.1016/S0261-5177(01)00050-4

    Article  Google Scholar 

  • Tucker CJ, Slayback DA, Pinzon JE, Los SO, Myneni RB, Taylor MG (2001) Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol 45(4):184–190. https://doi.org/10.1007/s00484-001-0109-8

    Article  Google Scholar 

  • Wang GX (1988) Land work manual. Shandong People’s Publishing House, Jinan (in Chinese)

    Google Scholar 

  • Wang CY (2008) Study on the relationship between aspect and slope stability. Dissertation, Kunming University of science and technology (in Chinese)

  • Wang M, Qiao J, Wu C (2008) Regional landslide danger assessments based on GIS and the root factor contributing weight model: a case study of the Wanzhou District Chongqing City, China. Geological bulleuin of China 27(11):1802–1809 (in Chinese)

    Google Scholar 

  • Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ 666:975–993. https://doi.org/10.1016/j.scitotenvol2019.02.263

    Article  Google Scholar 

  • Wang Z, Liu Q, Liu Y (2020) Mapping landslide susceptibility using machine learning algorithms and GIS: a case study in Shexian County, Anhui Province. China Symmetry 12(12):1954. https://doi.org/10.3390/sym12121954

    Article  Google Scholar 

  • Wang Y, Wen H, Sun D, Li Y (2021) Quantitative assessment of landslide risk based on susceptibility mapping using random forest and GeoDetector. Remote Sens 13(13):2625. https://doi.org/10.3390/rs13132625

    Article  Google Scholar 

  • Wei X, Zhang M, Xiao P (2006) Developmental characteristic, regularities of distribution of the slide, which is a geological disaster, in Baota district of Yanan city. Shanxi Arch 32(20):115–116 (in Chinese)

    Google Scholar 

  • Wu C, Qiao J (2005) The contributing rate research of slope aspect to landslide growth from Yunyang to Wushanin Three Gorges Reservoir Region. J Sichuan Univ (engineering Science Edition) 37(4):25–29 (in Chinese)

    Google Scholar 

  • Wu W, Sidle RC (1995) A distributed slope stability model for steep forested basins. Water Resour Res 31(8):2097–2110. https://doi.org/10.1029/95WR01136

    Article  Google Scholar 

  • Wu X, Lai C, Chen X, Ren X (2017) A landslide hazard assessment based on random forest weight: a case study in the Dongjiang River Basin. J Nat Disasters 26(5):119–129 (in Chinese)

    Google Scholar 

  • Wu Y, Ke Y, Chen Z, Liang S, Zhao H, Hong H (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. CATENA 187:104396. https://doi.org/10.1016/j.catena.2019.104396

    Article  Google Scholar 

  • Xie W, Guo Q, Wu JY, Li P, Yang H, Zhang M (2021) Analysis of loess landslide mechanism and numerical simulation stabilization on the Loess Plateau in Central China. Nat Hazards 106(1):805–827. https://doi.org/10.1007/s11069-020-04492-w

    Article  Google Scholar 

  • Xu X, Liu W, Gao P, Mu X (2003) The discussion on soil moisture distributional diversity in hilly Loess Plateau region. Ecol Environ 12(1):52–55 (in Chinese)

    Google Scholar 

  • Xu Z, Lin Z, Zhang M (2007) Loess and loess landslides in China. Chin J Rock Mech Eng 26(7):1297–1312 (in Chinese)

    Google Scholar 

  • Xu L, Dai F, Kuang G, Min H (2008) Types and characteristics of loess landslides at Heifangtai Loess Plateau China. J Mt Sci 26(3):364–371 (in Chinese)

    Google Scholar 

  • Xu C, Xu X, Wu Y, Dai F, Yao X, Yao Q (2013a) Detailed catalog of landslides triggered by the 2008 Wenchuan earthquake and statistical analyses of their spatial distribution. J Eng Geol 21(1):25–44 (in Chinese)

    Google Scholar 

  • Xu L, Dai F, Tu X, Tham LG, Zhou Y, Iqbal J (2013b) Landslides in a loess platform. North-West China Landslides 11(6):889–901. https://doi.org/10.1007/s10346-013-0445

    Article  Google Scholar 

  • Xu Q, Qi X, Xiu DH, Zhao KY (2019) Critical water level of abrupt loess landslides: a case study in Heifangtai. Gansu Province SHUILI XUEBAO 50(3):315–322 (in Chinese)

    Google Scholar 

  • Xu Y, Wang J, Gu T, Kong J (2020) Geological hazards in loess induced by agricultural irrigation in arid and semiarid regions of China. Adv Civ Eng. https://doi.org/10.1155/2020/8859166

    Article  Google Scholar 

  • Xue Q, Zhang M, Li L (2015) Loess landslide susceptibility evaluation based on slope unit and information value method in Bao⁃ta District. Yan’an Geol Bull China 34(11):2108–2115 (in Chinese)

    Google Scholar 

  • 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

    Google Scholar 

  • Yan ZZ, Zhong JX, Huang W et al (2018) Geological hazards in Ningxia. Ningxia people’s Publishing House, Yinchuan (in Chinese)

    Google Scholar 

  • Yang BA, Zhang KJ (2008) Research on theory, method and application of multi objective decision analysis. Donghua University Press, Shanghai (in Chinese)

    Google Scholar 

  • Yin YP, Hu SY, Shi SW, Zhao SJ (2018) Technical guide for landslide prevention and control. Geological Publishing House, Beijing (in Chinese)

    Google Scholar 

  • Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856. https://doi.org/10.1007/s10346-015-0614-1

    Article  Google Scholar 

  • Yu C, Chen J (2020) Application of a GIS-based slope unit method for landslide susceptibility mapping in Helong City: comparative assessment of ICM, AHP, and RF model. Symmetry 12(11):1848. https://doi.org/10.3390/sym12111848

    Article  Google Scholar 

  • Yuan X, Zhao F, Chen X, Cheng X, Yao X (2017) Zoning of geological hazards’ susceptibility evaluation in Suide county of Shaanxi province. J Catastrophol 32(1):117–120

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Google Scholar 

  • Zhang J (2000) Fuzzy analytical hierarchy process. Fuzzy Syst Math 14(2):80–88 (in Chinese)

    Google Scholar 

  • Zhang M, Li T (2011) Triggering factors and forming mechanism of loess landslides. J Eng Geol 19(4):530–540 (in Chinese)

    Google Scholar 

  • Zhang L, Ni WK, Zhang Q et al (2010) Geological disaster in Pengyang County, Ningxia. Ningxia People’s Publishing House, Yinchuan (in Chinese)

    Google Scholar 

  • Zhang Y, Qin S, Zhai J, Li G, Peng S, Liu X, Chen J (2018) Susceptibility assessment of debris flow based on GIS and weight information for the Changbai mountain area. Hydrogeol Eng Geol 45(2):150–158 (in Chinese)

    Google Scholar 

  • Zhou S, Zhou S, Tan X (2020) Nationwide susceptibility mapping of landslides in Kenya using the fuzzy analytic hierarchy process model. Land 9(12):535. https://doi.org/10.3390/land9120535

    Article  Google Scholar 

  • Zhu K, Zhang X, Xiao L (1997) The METHOD AND APPLICATIONS OF FUZZY AHP. Theory and Pract Syst Eng 16(12):65–70 (in Chinese)

    Google Scholar 

  • Zhuang J, Peng J, Wang G, Javed I, Wang Y, Li W (2018) Distribution and characteristics of landslide in Loess Plateau: a case study in Shaanxi province. Eng Geol 236:89–96. https://doi.org/10.1016/j.enggeo.2017.03.001

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Ningxia Hui Autonomous Region Key Research and Development Plan (Science and Technology Support Plan) (2020BEG03023); Shaanxi Provincial Key Research and Development Plan (2020SF-379); Scientific Research Plan for Local Special Service of Shaanxi Provincial Education Department (19JC027).

Author information

Authors and Affiliations

  1. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, China

    Zhengjun Mao & Shuojie Shi

  2. Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an, 710054, China

    Zhengjun Mao

  3. Geological Research Institute for Coal Green Mining, Xi’an University of Science and Technology, Xi’an, 710054, China

    Zhengjun Mao

  4. Institute of Remote Sensing & Mapping, Ningxia Hui Autonomous Region (Remote Sensing Center of Ningxia Hui Autonomous Region), Guyuan, China

    Huan Li & Jiewen Sun

  5. Ningxia Hui Autonomous Region Institute of Survey and Monitoring of Land and Resources, Guyuan, China

    Jiaxin Zhong

Authors
  1. Zhengjun Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuojie Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiaxin Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiewen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengjun Mao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mao, Z., Shi, S., Li, H. et al. Landslide susceptibility assessment using triangular fuzzy number-analytic hierarchy processing (TFN-AHP), contributing weight (CW) and random forest weighted frequency ratio (RF weighted FR) at the Pengyang county, Northwest China. Environ Earth Sci 81, 86 (2022). https://doi.org/10.1007/s12665-022-10193-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-022-10193-3

Keywords

Search

Navigation