Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A modified frequency ratio method for landslide susceptibility assessment

  • Technical Note
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

The frequency ratio method is one of the most widely adopted methods for landslide susceptibility assessment. However, due to the obligatory classifications of landslide-related factors with continuous factor values, the conventional frequency ratio method is complicated by a discontinuity problem of the frequency ratio values and a subjectivity problem. This paper has modified the conventional frequency ratio method and developed a handy geographical information system extension that implements the modified method. Through calculating the frequency ratios for every “identical normalized factor value” instead of for every “factor class,” the modified method radically increased the continuity of frequency ratio values and reduced the subjectivity accompanied by the classifications of factors. An automatic and quick assessment of landslide susceptibility becomes possible because the calculations of frequency ratios for different factors in the modified method are constrained by only two uniform parameters (precision and bin width). Two case studies were adopted to inspect the performances of the modified method. From a quantitative point of view, the modified method derives landslide susceptibility models having slightly larger AUC values than the conventional method. From a qualitative point of view, the modified method gives much more detailed variations of frequency ratio with factor value and, as a result, can reveal characteristic fluctuations of frequency ratio and can smoothen the spatial discontinuity of the landslide susceptibility map derived by the conventional method. In practice, this modified frequency ratio method is expected to benefit the landslide susceptibility assessment and get further evaluations in the meantime.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

References

  • Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12:1077–1095. doi:10.1007/s10346-014-0521-x

    Article  Google Scholar 

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106. doi:10.1007/s10346-011-0283-7

    Article  Google Scholar 

  • 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. Environ Geol 54:1127–1143. doi:10.1007/s00254-007-0882-8

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31. doi:10.1016/j.geomorph.2004.06.010

    Article  Google Scholar 

  • Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31. doi:10.1016/j.geomorph.2009.09.025

    Article  Google Scholar 

  • Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831. doi:10.5194/nhess-13-2815-2013

    Article  Google Scholar 

  • Chacón J, Irigaray C, Fernández T, El Hamdouni R (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65:341–411. doi:10.1007/s10064-006-0064-z

    Article  Google Scholar 

  • Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H (2016) A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab J Geosci 9:204. doi:10.1007/s12517-015-2150-7

    Article  Google Scholar 

  • Chung C-J, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472. doi:10.1023/B:NHAZ.0000007172.62651.2b

    Article  Google Scholar 

  • Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. doi:10.1007/s10064-013-0538-8

    Google Scholar 

  • Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci 12:327–340. doi:10.5194/nhess-12-327-2012

    Article  Google Scholar 

  • Cruden DM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides: investigation and mitigation, Transport Res Board Spec Rep 247, pp 36–75

  • Ding QF, Chen W, Hong HY (2016) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int. doi:10.1080/10106049.2016.1165294

    Google Scholar 

  • Erener A, Düzgün HSB (2010) Modification of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68. doi:10.1007/s10346-009-0188-x

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66:327–343. doi:10.1016/j.geomorph.2004.09.025

    Article  Google Scholar 

  • Fressard M, Thiery Y, Maquaire O (2014) Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the pays d’Auge plateau hillslopes (Normandy, France). Nat Hazards Earth Syst Sci 14:569–588. doi:10.5194/nhess-14-569-2014

    Article  Google Scholar 

  • Guo CB, Montgomery DR, Zhang YS, Wang K, Yang ZH (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110. doi:10.1016/j.geomorph.2015.07.012

    Article  Google Scholar 

  • Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micub M, van Westen C, Bălteanu B (2016) Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology 253:508–523. doi:10.1016/j.geomorph.2015.10.030

    Article  Google Scholar 

  • Ilanloo M (2011) A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: an experience of Karaj dam basin in Iran. Procedia Soc Behav Sci 19:668–676. doi:10.1016/j.sbspro.2011.05.184

    Article  Google Scholar 

  • Irigaray C, Fernández T, El Hamdouni R, Chacón J (2007) Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards 41:61–79. doi:10.1007/s11069-006-9027-8

    Article  Google Scholar 

  • Kannan M, Saranathan E, Anbalagan R (2013) Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India. Arab J Geosci 6:2901–2913. doi:10.1007/s12517-012-0587-5

    Article  Google Scholar 

  • Kayastha P (2015) Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from Garuwa sub-basin, East Nepal. Arab J Geosci 8:8601–8613. doi:10.1007/s12517-015-1831-6

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat Hazards 63:479–498. doi:10.1007/s11069-012-0163-z

    Article  Google Scholar 

  • Kayastha P, Bijukchhen SM, Dhital MR, De Smedt F (2013) GIS based landslide susceptibility mapping using a fuzzy logic approach: a case study from Ghurmi-Dhad Khola area, Eastern Nepal. J Geol Soc India 82:249–261. doi:10.1007/s12594-013-0147-y

    Article  Google Scholar 

  • Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30:26–33. doi:10.1111/gto.12034

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491. doi:10.1080/01431160412331331012

    Article  Google Scholar 

  • Lee S, Choi J (2004) Landslide susceptibility mapping using GIS and the weight-of-evidence model. Int J Geogr Inf Sci 18:789–814. doi:10.1080/13658810410001702003

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41. doi:10.1007/s10346-006-0047-y

    Article  Google Scholar 

  • Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990. doi:10.1007/s00254-005-1228-z

    Article  Google Scholar 

  • Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Process Landf 27:1361–1376. doi:10.1002/esp.593

    Article  Google Scholar 

  • Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides 4:327–338. doi:10.1007/s10346-007-0088-x

    Article  Google Scholar 

  • Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234. doi:10.1016/j.enggeo.2011.09.006

    Article  Google Scholar 

  • Mathew J, Jha VK, Rawat GS (2009) Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides 6:17–26. doi:10.1007/s10346-008-0138-z

    Article  Google Scholar 

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236. doi:10.1016/j.jseaes.2012.10.005

    Article  Google Scholar 

  • Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71:523–547. doi:10.1007/s11069-013-0932-3

    Article  Google Scholar 

  • 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. doi:10.1016/j.jseaes.2012.12.014

    Article  Google Scholar 

  • Park NW (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62:367–376. doi:10.1007/s12665-010-0531-5

    Article  Google Scholar 

  • Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464. doi:10.1007/s12665-012-1842-5

    Article  Google Scholar 

  • Parise M, Jibson RW (2000) A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Eng Geol 58:251–270. doi:10.1016/S0013-7952(00)00038-7

    Article  Google Scholar 

  • Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38:301–320. doi:10.1007/s12524-010-0020-z

    Article  Google Scholar 

  • Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054. doi:10.1007/s12665-009-0245-8

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30. doi:10.1007/s10346-009-0183-2

    Article  Google Scholar 

  • Ramesh V, Anbazhagan S (2015) Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models. Environ Earth Sci 73:8009–8021. doi:10.1007/s12665-014-3954-6

    Article  Google Scholar 

  • Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742. doi:10.1007/s12517-012-0807-z

    Article  Google Scholar 

  • Regmi AD, Poudel K (2016) Assessment of landslide susceptibility using GIS-based evidential belief function in Patu Khola watershed, Dang, Nepal. Environ Earth Sci 75:743. doi:10.1007/s12665-016-5562-0

    Article  Google Scholar 

  • Sarkar S, Roy AK, Martha TR (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. J Geol Soc India 82:351–362. doi:10.1007/s12594-013-0162-z

    Article  Google Scholar 

  • Son J, Suh J, Park H-D (2016) GIS-based landslide susceptibility assessment in Seoul, South Korea, applying the radius of influence to frequency ratio analysis. Environ Earth Sci 75:310. doi:10.1007/s12665-015-5149-1

    Article  Google Scholar 

  • Thiery Y, Malet J-P, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92:38–59. doi:10.1016/j.geomorph.2007.02.020

    Article  Google Scholar 

  • Thiery Y, Maquaire O, Fressard M (2014) Application of expert rules in indirect approaches for landslide susceptibility assessment. Landslides 11:411–424. doi:10.1007/s10346-013-0390-8

    Article  Google Scholar 

  • Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through a artificial neural network classifier. Nat Hazards 74:1489–1516. doi:10.1007/s11069-014-1245-x

    Article  Google Scholar 

  • Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection, Greece. Landslides 13:305–320. doi:10.1007/s10346-015-0565-6

    Article  Google Scholar 

  • Vakhshoori V, Zare M (2016) Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics Nat Hazards Risk 7:1731–1752. doi:10.1080/19475705.2016.1144655

    Article  Google Scholar 

  • van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Env 65:167–184. doi:10.1007/s10064-005-0023-0

    Article  Google Scholar 

  • Wang CL, Zhao N, Yue TX, Zhao MW, Chen C (2015b) Change trend of monthly precipitation in China with an improved surface modeling method. Environ Earth Sci 74:6459–6469. doi:10.1007/s12665-014-4012-0

    Article  Google Scholar 

  • Wang L-J, Sawada K, Moriguchi S (2013) Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Comput Geosci 57:81–92. doi:10.1016/j.cageo.2013.04.006

    Article  Google Scholar 

  • Wang L-J, Guo M, Sawada K, Lin J, Zhang JC (2016) A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J 20:117–136. doi:10.1007/s12303-015-0026-1

    Article  Google Scholar 

  • Wang QQ, Wang DC, Huang Y, Wang ZH, Zhang LH, Guo QZ, Chen W, Chen WG, Sang MQ (2015a) Landslide susceptibility mapping based on selected optimal combination of landslide predisposing factors in a large catchment. Sustainability 7:16653–16669. doi:10.3390/su71215839

    Article  Google Scholar 

  • Xu C, Xu XW, Dai FC, Saraf Arun K (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. doi:10.1016/j.cageo.2012.01.002

    Article  Google Scholar 

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582. doi:10.1016/j.geomorph.2008.02.011

    Article  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 85:274–287. doi:10.1016/j.catena.2011.01.014

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks method in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266. doi:10.1016/j.enggeo.2005.02.002

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138. doi:10.1016/j.cageo.2008.08.007

    Article  Google Scholar 

  • Youssef AM (2015) Landslide susceptibility delineation in the Ar-Rayth area, Jizan, Kingdom of Saudi Arabia, by using analytical hierarchy process, frequency ratio, and logistic regression models. Environ Earth Sci 73:8499–8518. doi:10.1007/s12665-014-4008-9

    Article  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 13:839–856. doi:10.1007/s10346-015-0614-1

Download references

Acknowledgments

This study is supported by the National Science Foundation of China (NO. 41525010, 41421001, 41272354 and 41402321) the China Geological Survey Project (NO. 12120113038000) and Research Foundation for Youth Scholars of IGSNRR, CAS. The CNIC, CAS, is appreciated for providing the SRTM data. The LP DAAC is appreciated for providing the MOD13Q1 data. Dr. Zhao N and Prof. Yue TX are appreciated for providing the grid precipitation data. The Fujian Geological Environment Monitoring Center is appreciated for providing the SPOT images and the DEM of the Caiyuan Basin. Mr. Wang ZW is particularly appreciated for helping to prepare the landslide dataset of the Caiyuan Basin. The comments of two anonymous reviewers were very helpful in improving the manuscript.

Author information

Authors and Affiliations

  1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing, 100101, China

    Langping Li, Hengxing Lan, Quanwen Li & Yuming Wu

  2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, T6G 2R3, Canada

    Hengxing Lan

  3. Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, 100081, China

    Changbao Guo & Yongshuang Zhang

  4. Key Laboratory of Neotectonic Movement and Geohazard, Ministry of Land and Resources, Beijing, 100081, China

    Changbao Guo & Yongshuang Zhang

  5. University of Chinese Academy of Sciences, Beijing, 100049, China

    Quanwen Li

Authors
  1. Langping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hengxing Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changbao Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongshuang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Quanwen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuming Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hengxing Lan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Lan, H., Guo, C. et al. A modified frequency ratio method for landslide susceptibility assessment. Landslides 14, 727–741 (2017). https://doi.org/10.1007/s10346-016-0771-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-016-0771-x

Keywords

Search

Navigation