Skip to main content

Advertisement

SpringerLink
Log in

Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Due to the unique blend of physiography, climate, and socio-economic factors, the windward slopes of the southern Western Ghats (WG) are highly susceptible to the occurrence of landslides. The present study compares the efficiency of advanced deep learning and machine learning techniques, such as deep neural network (DNN), and random forest (RF) with conventional methods, viz., frequency ratio (FR) and analytical hierarchy process (AHP) method for mapping the landslide susceptibility in a highland region of the western slopes of the southern WG, where the field observations are limited. Various factors controlling the occurrence of landslides, such as lithology, geomorphology, land use/ land cover, soil texture, proximity to lineaments, roads, and streams, slope angle, rainfall, topographic wetness index (TWI) and curvature are used for the landslide susceptibility mapping. The results of the present study indicate comparable performances between machine learning models and conventional approaches in field data-scarce regions. Moreover, this study has extended implications in landslide risk management of the southern WG in the context of repeated extreme rainfall events.

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

Similar content being viewed by others

Data availability

Data will be available on a personal request.

References

  • Achu AL, Aju CD, Reghunath R (2020) Spatial modelling of shallow landslide susceptibility: a study from the southern Western Ghats region of Kerala, India. Ann GIS, 1–19. https://doi.org/10.1080/19475683.2020.1758207

  • Achu AL, Joseph S, Aju CD, Mathai J (2021) Preliminary analysis of a catastrophic landslide event on 6 August 2020 at Pettimudi, Kerala State India. Landslides 18(4):1459–1463. https://doi.org/10.1007/s10346-020-01598-x

    Article  Google Scholar 

  • Achu AL, Aju CD, Pham QB, Reghunath R, Anh DT (2022) Landslide susceptibility modelling using hybrid bivariate statistical-based machine-learning method in a highland segment of Southern Western Ghats India. Environ Earth Sci 81(13):1–18. https://doi.org/10.1007/s12665-022-10464-z

    Article  Google Scholar 

  • AGU (2017) The Human Cost of Landslide in 2016, the Landslide Blog. American Geophysical Union (AGU). http://blogs.agu.org/landslideblog/

  • Akgun A, Türk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environ Earth Sci 61(3):595–611. https://doi.org/10.1007/s12665-009-0373-1

    Article  Google Scholar 

  • Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36. https://doi.org/10.1016/j.catena.2013.10.011

    Article  Google Scholar 

  • Anbazhagan S, Sajinkumar KS (2011) Geoinformatics in terrain analysis and landslide susceptibility mapping in parts of Western Ghats, India. Geoinformatics in applied geomorphology. CRC Press, Boca Raton, pp 291–315

  • 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 Lithol 81(4):432–445. https://doi.org/10.1016/j.enggeo.2005.08.004

  • Barredo J, Benavides A, Hervás J, van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J Appl Earth Obs Geoinf 2(1):9–23

    Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J 24(1):43–69. https://doi.org/10.1080/02626667909491834

    Article  Google Scholar 

  • Booth GD, Niccolucci MJ, Schuster EG (1994) Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. Research paper INT (USA)

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Brenning A, Schwinn M, Ruiz-Páez AP, Muenchow J (2014) Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador Loja Province. NHESD 2(3):1945–1975. https://doi.org/10.5194/nhess-15-45-2015

    Article  Google Scholar 

  • Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378. https://doi.org/10.1007/s10346-015-0557-6

    Article  Google Scholar 

  • Candel A, Parmar V, LeDell E, Arora A (2016) Deep learning with H2O. H2O. ai Inc, pp 1–21

  • Chen W, Shahabi H, Shirzadi A, Li T, Guo C, Hong H, Li W, Pan D, Hui J, Ma M, Xi M (2018) A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment. Geocarto Int 1–23. https://doi.org/10.1080/10106049.2018.1425738

  • Chen W, Sun Z, Han J (2019) Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models. Appl Sci 9(1):171

    Article  Google Scholar 

  • Chung CJF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65(12):1389–1399

    Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, 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. https://doi.org/10.1007/s11069-012-0347-6

    Article  Google Scholar 

  • Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int 32(6):619–639. https://doi.org/10.1080/10106049.2016.1165294

    Article  Google Scholar 

  • Dou J, Yunus AP, Merghadi A, Shirzadi A, Nguyen H, Hussain Y, Avtar R, Chen Y, Pham BT, Yamagishi H (2020) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ 720:137320

    Article  Google Scholar 

  • Faber FA, Lindmaa A, Von Lilienfeld OA, Armiento R (2016) Machine learning energies of 2 million elpasolite (A B C 2 D 6) crystals. Phys Rev Lett 117(13):135502

    Article  Google Scholar 

  • Feby B, Achu AL, Jimnisha K, Ayisha VA, Reghunath R (2020) Landslide susceptibility modelling using integrated evidential belief function based logistic regression method: A study from Southern Western Ghats, India. Remote Sens Appl Soc Environ 20:100411

    Google Scholar 

  • Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18(8). https://doi.org/10.5194/nhess-18-2161-2018

  • Goodfellow I, Warde-Farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. In: International conference on machine learning. PMLR, pp 1319–1327

  • Gorsevski PV, Jankowski P, Gessler PE (2006) A heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control Cybern 35:121–146

    Google Scholar 

  • Gostelow TP (1996) Landslides. In: Hydrology of Disasters, pp 183–230. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8680-1_8

  • Guha-Sapir D, Below R, Hoyois PH (2009) EM-DAT: The CRED/OFDA International Disaster Database–www.emdat.be.Brussels. Belgium: Université Catholique de Louvain

  • Guru B, Veerappan R, Sangma F, Bera S (2017) Comparison of probabilistic and expert-based models in landslide susceptibility zonation mapping in part of Nilgiri District, Tamil Nadu India. Spat Inf Res 25(6):757–768. https://doi.org/10.1007/s41324-017-0143-1

    Article  Google Scholar 

  • Hao L, Rajaneesh A, van Westen C, Sajinkumar KS, Martha TR, Jaiswal P, McAdoo BG (2020) Constructing a complete landslide inventory dataset for the 2018 Monsoon disaster in Kerala, India, for land use change analysis. Earth Syst Sci Data 12(4):2899–2918

    Article  Google Scholar 

  • Johnson PA, McCuen RH (1996) Mud and debris flows. In Hydrology of Disasters, pp 161–181. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8680-1_7

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multicriteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439. https://doi.org/10.1007/s10346-013-0391-7

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.cageo.2012.11.003

    Article  Google Scholar 

  • Korup O, Stolle A (2014) Landslide prediction from machine learning. Geol Today 30(1):26–33

    Article  Google Scholar 

  • Krishnan MVN, Pratheesh P, Rejith PG, Vijith H (2015) Determining the suitability of two different statistical techniques in shallow landslide (debris flow) initiation susceptibility assessment in the western Ghats. Environ Res Eng Manag 70(4):26–39. https://doi.org/10.5755/j01.erem.70.4.8510

    Article  Google Scholar 

  • Kumar BM (2006) Land use in Kerala: changing scenarios and shifting paradigms. J Trop Agric 43:1–12

    Google Scholar 

  • Kuriakose SL, Sankar G, Muraleedharan C (2009) History of landslide susceptibility and a chorology of landslide-prone areas in the Western Ghats of Kerala India. Environ Geol 57(7):1553–1568. https://doi.org/10.1007/s00254-008-1431-9

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Lithol 50(6):847–855. https://doi.org/10.1007/s00254-006-0256-7

    Article  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

    Article  Google Scholar 

  • Lupiano V, Rago V, Terranova OG, Iovine G (2019) Landslide inventory and main geomorphological features affecting slope stability in the Picentino river basin (Campania, southern Italy). J Maps 15(2):131–141. https://doi.org/10.1080/17445647.2018.1563836

    Article  Google Scholar 

  • Martha TR, Roy P, Khanna K, Mrinalni K, Kumar KV (2019) Landslides mapped using satellite data in the Western Ghats of India after excess rainfall during August 2018. Curr Sci 117(5):804

    Article  Google Scholar 

  • Meten M, PrakashBhandary N, Yatabe R (2015) Effect of landslide factor combinations on the prediction accuracy of landslide susceptibility maps in the Blue Nile Gorge of Central Ethiopia. Geoenvironmental Disasters 2(1):9. https://doi.org/10.1186/s40677-015-0016-7

    Article  Google Scholar 

  • Moeyersons J, Tréfois P, Lavreau J, Alimasi D, Badriyo I, Mitima B, Mundala M, Munganga DO, Nahimana L (2004) A geomorphological assessment of landslide origin at Bukavu, Democratic Republic of the Congo. Eng Geol 72(1–2):73–87. https://doi.org/10.1016/j.enggeo.2003.06.003

    Article  Google Scholar 

  • Mugagga F, Kakembo V, Buyinza M (2012) Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. CATENA 90:39–46. https://doi.org/10.1016/j.catena.2011.11.004

    Article  Google Scholar 

  • Nhu VH, Hoang ND, Nguyen H, Ngo PTT, Bui TT, Hoa PV, Samui P, Bui DT (2020) Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. CATENA 188:104458

    Article  Google Scholar 

  • Pham QB, Achour Y, Ali SA, Parvin F, Vojtek M, Vojteková J, Al-Ansari N, Achu AL, Costache R, Khedher KM, Anh DT (2021) A comparison among fuzzy multicriteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomat Nat Haz Risk 12(1):1741–1777

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin Iran. Catena 97:71–84. https://doi.org/10.1016/j.catena.2012.05.005

    Article  Google Scholar 

  • Pradhan AMS, Kim YT (2020) Rainfall-induced shallow landslide susceptibility mapping at two adjacent catchments using advanced machine learning algorithms. ISPRS Int J Geo Inf 9(10):569

    Article  Google Scholar 

  • Pradhan B, Oh HJ, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Haz Risk 1(3):199–223. https://doi.org/10.1080/19475705.2010.498151

    Article  Google Scholar 

  • Ramasamy SM, Gunasekaran S, Saravanavel J, Joshua RM, Rajaperumal R, Kathiravan R, Palanivel K, Muthukumar M (2020) Geomorphology and landslide proneness of Kerala, India a geospatial study. Landslides, pp 1–14. https://doi.org/10.1007/s10346-020-01562-9

  • Saaty TL (1980) The Analytical Hierarchy Process, Planning, Priority. Resource Allocation. RWS Publications, USA

  • Schmidt J, Shi J, Borlido P, Chen L, Botti S, Marques MA (2017) Predicting the thermodynamic stability of solids combining density functional theory and machine learning. Chem Mater 29(12):5090–5103

    Article  Google Scholar 

  • Sharma S, Mahajan AK (2018) A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. Bull Eng Lithol Environ 1–18. https://doi.org/10.1007/s10064-018-1259-9

  • Sidle RC, Ochiai H (2007) Landslides processes, prediction, and land use water resources monograph 18. In: Natural Resources Forum (vol 31, pp 322–326)

  • Soman K (1987) Geology of Kerala. GSI Publications, 2(1)

  • SSO (2007) Benchmark soils of Kerala. Soil Survey Organization, Government of Kerala, Thiruvananthapuram

  • Stocking MA (1972) Relief analysis and soil erosion in Rhodesia using multivariate techniques. Zeitschrift Fur Geomorphol NF 16:432–443

    Google Scholar 

  • Sun D, Xu J, Wen H, Wang D (2021) Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest. Eng Geol 281:105972

    Article  Google Scholar 

  • Sze V, Chen YH, Yang TJ, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329

    Article  Google Scholar 

  • Tanyu BF, Abbaspour A, Alimohammadlou Y, Tecuci G (2021) Landslide susceptibility analyses using Random Forest, C4. 5, and C5. 0 with balanced and unbalanced datasets. CATENA, 203, p 105355

  • Thampi PK, Mathai J, Sankar G (1995) A regional evaluation of landslide prone areas in the Western Ghats of Kerala. In: Abstracts of the national seminar on landslides in Western Ghats, 29–30 Aug 1995. Centre for Earth Science Studies, Government of Kerala, Thiruvananthapuram, India

  • Thampi PK, Mathai J, Sankar G, Sidharthan S (1997) Evaluation study in terms of landslide mitigation in parts of Western Ghats, Kerala, Technical report. Center for Earth Science Studies, Thiruvananthapuram

  • Tiyasha, Tung TM, Yaseen ZM (2021) Deep Learning for Prediction of Water Quality Index Classification: Tropical Catchment Environmental Assessment. Natural Resources Research, pp 1–20

  • Vergani C, Giadrossich F, Buckley P, Conedera M, Pividori M, Salbitano F, Rauch HS, Lovreglio R, Schwarz M (2017) Root reinforcement dynamics of European coppice woodlands and their effect on shallow landslides: a review. Earth Sci Rev 167:88–102. https://doi.org/10.1016/j.earscirev.2017.02.002

    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 Lithol 55(7):1397–1405. https://doi.org/10.1007/s00254-007-1090-2

    Article  Google Scholar 

  • Vijith H, Krishnakumar KN, Pradeep GS, Ninu Krishnan MV, Madhu G (2014) Shallow landslide initiation susceptibility mapping by GIS-based weights-of-evidence analysis of multi-class spatial datasets: a case study from the natural sloping terrain of Western Ghats, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 8(1):pp.48–62. https://doi.org/10.1080/17499518.2013.843437

  • Vijith H, Rejith PG, Madhu G (2009) Using InfoVal method and GIS techniques for the spatial modelling of landslide susceptibility in the upper catchment of river Meenachil in Kerala. J Indian Soc Remote Sens 37(2):241–250

    Article  Google Scholar 

  • Walker LR, Shiels AB (2013) Physical causes and consequences for Landslide Ecology

  • Wang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci 138:104445

    Article  Google Scholar 

  • Youssef AM, Al-Kathery M, Pradhan B (2015) Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19(1):113–134. https://doi.org/10.1007/s12303-014-0032-8

    Article  Google Scholar 

  • Zhang Y, Ling C (2018) A strategy to apply machine learning to small datasets in materials science. NPJ Comput Mater 4(1):1–8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Climate Variability and Aquatic Ecosystems, Kerala University of Fisheries and Ocean Studies (KUFOS), Kochi, 682 508, Kerala, India

    Ashokan Laila Achu & Girish Gopinath

  2. Department of Geological and Mining Engineering and Sciences, Michigan Technological University, Houghton, MI, 49931, USA

    Jobin Thomas

  3. Department of Geology, University of Kerala, Thiruvananthapuram, 695 581, Kerala, India

    Chandrika Dhanapalan Aju

  4. Geological Survey of India (GSI), Kolkata, India

    Praveen Kolappapillai Remani

Authors
  1. Ashokan Laila Achu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jobin Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandrika Dhanapalan Aju
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Praveen Kolappapillai Remani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Girish Gopinath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashokan Laila Achu.

Ethics declarations

Conflicts of interest

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.

Additional information

Communicated by H. Babaie.

Publisher's note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 40 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Achu, A.L., Thomas, J., Aju, C.D. et al. Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data. Earth Sci Inform 16, 1025–1039 (2023). https://doi.org/10.1007/s12145-022-00910-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00910-8

Keywords

Search

Navigation