Skip to main content

Advertisement

SpringerLink
Log in

Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods

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

Abstract

Landslide susceptibility mapping (LSM) assists identifying and targeting landslide preventive measures, thereby minimizing potential losses. Multiple approaches are employed for LSM in various physiographic regions; however, their applicability has differed across studies, with limited understanding on the most suitable approach for LSM in high mountain areas. Hence, we conducted LSM in the Indrawati watershed, a high mountain area of Central Nepal, employing four approaches: frequency ratio, logistic regression, artificial neural network, and support vector machine. Nine landslide causal factors (slope, aspect, elevation, geological formation, proximity to river, proximity to road, land cover, soil type, and curvature) were considered for LSM. Rainfall-induced landslides were mapped by the on-screen digitization of satellite images and field observations. The landslides were randomly split into a ratio of 80:20 for training and validating the susceptibility maps. The LSMs obtained by four methods were then validated and compared using area under curve (AUC), kappa index, and statistical inferences (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy). Our study showed that Eutric Cambisols, a class of soil type, has a strong association with landslide occurrence among the 52 classes of the nine causal factors. We found that the artificial neural network approach possessed the best prediction capability (AUC value = 86.9%) among the four methods, followed by logistic regression (85.6%), support vector machine (81.2%), and frequency ratio (80.1%) approaches. However, Kappa index and other statistical inferences suggested the support vector machine approach to be the second-best method. Overall, we found that the artificial neural network yields more accurate and reliable results and hence considered as a promising approach for susceptibility mapping in high mountainous region of Hindu-Kush Himalaya. The findings of this study might be useful for landslide analysts, development planners and decision-makers in conducting LSM and development planning in high mountain regions.

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

Availability of data and material

Data will be provided upon request.

Code availability

NA.

References

  • Abe S (2010) Feature selection and extraction. In: Support vector machines for pattern classification. Advances in pattern recognition. Springer, London, pp. 331–341

  • Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111

    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(1):93–106

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44

    Article  Google Scholar 

  • Alkhasawneh MS, Ngah UK, Tay LT, Isa NAM (2014) Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network. Environ Earth Sci 72(3):787–799

    Article  Google Scholar 

  • Ambrosi C, Strozzi T, Scapozza C, Wegmüller U (2018) Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on earth-observation data. Eng Geol 237:217–228

    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(1–2):15–31

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81

    Article  Google Scholar 

  • Ba Q, Chen Y, Deng S, Yang J, Li H (2018) A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment. Earth Sci Inf 11(3):373–388

    Article  Google Scholar 

  • Bai SB, Wang J, Lu GN et al (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three Gorges area, China. Geomorphology 115:23–31

    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)

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation

  • Brown SR, Scholz CH, Rundle JB (1991) A simplified spring-block model of earthquakes. Geophys Res Lett 18(2):215–218

    Article  Google Scholar 

  • Budimir MEA, Atkinson PM, Lewis HG (2015) A systematic review of landslide probability mapping using logistic regression. Landslides 12(3):419–436

    Article  Google Scholar 

  • Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) 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

    Article  Google Scholar 

  • Carrara A, Pike R (2008) GIS technology and models for assessing landslide hazard and risk. Geomorphology (Amsterdam) 94(3–4)

  • Chalkias C, Ferentinou M, Polykretis C (2014) GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece. Geosciences 4(3):176–190

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017) Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327

    Article  Google Scholar 

  • Chen W, Wang J, Xie X, Hong H, Van Trung N, Bui DT, Li X (2016) Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environ Earth Sci 75(20):1344

    Article  Google Scholar 

  • Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, Van Den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73(2):209–263

    Google Scholar 

  • Cui Y, Cheng D, Choi CE, Jin W, Lei Y, Kargel JS (2019) The cost of rapid and haphazard urbanization: lessons learned from the Freetown landslide disaster. Landslides 16(6):1167–1176

    Article  Google Scholar 

  • Dahal RK, Hasegawa S (2008) Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology 100(3–4):429–443

    Article  Google Scholar 

  • Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):65–87

    Article  Google Scholar 

  • Del Soldato M, Di Martire D, Bianchini S, Tomás R, De Vita P, Ramondini M, Calcaterra D (2019) Assessment of landslide-induced damage to structures: the Agnone landslide case study (southern Italy). Bull Eng Geol Environ 78(4):2387–2408

    Article  Google Scholar 

  • Dijkshoorn JA, Huting JRM (2009) Soil and terrain database for Nepal (1.1 million) (No. 2009/01). ISRIC-World Soil Information

  • Driessen PM (ed) (1991) The major soils of the world: Lecture notes on their geography, formation, properties and use. Agricultural University

  • Fausett LV (2006) Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India

  • Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) On behalf of the JTC-1 Joint Technical Committee on Landslides and Engineered Slopes (2008) guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102(3–4):85–98

    Article  Google Scholar 

  • Froude MJ, Petley D (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazard 18:2161–2181

    Article  Google Scholar 

  • Gnyawali KR, Zhang Y, Wang G, Miao L, Pradhan AMS, Adhikari BR, Xiao L (2020) Mapping the susceptibility of rainfall and earthquake triggered landslides along China-Nepal highways. Bull Eng Geol Env 79(2):587–601

    Article  Google Scholar 

  • Günther F, Fritsch S (2010) neuralnet: training of neural networks. R J 2(1):30–38

    Article  Google Scholar 

  • Hoehler FK (2000) Bias and prevalence effects on kappa viewed in terms of sensitivity and specificity. J Clin Epidemiol 53(5):499–503

    Article  Google Scholar 

  • Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA 133:266–281

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York

    Book  Google Scholar 

  • Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529

    Article  Google Scholar 

  • ICIMOD (2013) Land cover of Nepal 2010. Kathmandu, Nepal: ICIMOD. http://apps.geoportal.icimod.org/ArcGIS/rest/services/Nepal/Landcover2010/MapServer/0. Assessed 15 Dec 2018.

  • Jaafari A, Rezaeian J, Omrani MSO (2017) Spatial prediction of slope failures in support of forestry operations safety. Croat J For Eng 38(1):107–118

    Google Scholar 

  • Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab—an S4 package for kernel methods in R. J Stat Softw 11(9):1–20

    Article  Google Scholar 

  • Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Observ Geoinf 11(5):352–359

    Google Scholar 

  • Kavzoglu T, Mather PM (2003) The use of backpropagating artificial neural networks in land cover classification. Int J Remote Sens 24(23):4907–4938

    Article  Google Scholar 

  • Kargel JS, Leonard GJ, Shugar DH, Haritashya UK, Bevington A, Fielding EJ, Fujita K, Geertsema M, Miles ES, Steiner J, Anderson E (2016) Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake. Science 351(6269)

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41

    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 Geol 50(6):847–855

    Article  Google Scholar 

  • Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47(7):982–990

    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(4):327–338

    Article  Google Scholar 

  • Li G, Lei Y, Yao H, Wu S, Ge J (2017) The influence of land urbanization on landslides: an empirical estimation based on Chinese provincial panel data. Sci Total Environ 595:681–690

    Article  Google Scholar 

  • Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47(3):411–435

    Article  Google Scholar 

  • Mancini F, Ceppi C, Ritrovato G (2010) GIS and statistical analysis for landslide susceptibility mapping in the Daunia area (Italy)

  • Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234

    Article  Google Scholar 

  • McAdoo BG, Quak M, Gnyawali KR, Adhikari BR, Devkota S, Rajbhandari PL, Sudmeier-Rieux K (2018) Roads and landslides in Nepal: how development affects environmental risk. Nat Hazard 18(12):3203–3210

    Article  Google Scholar 

  • McFadden D (1977) Quantitative methods for analyzing travel behavior of individuals: some recent developments. Institute of Transportation Studies, University of California, Berkeley

    Google Scholar 

  • Menard S (1995) An introduction to logistic regression diagnostics. Appl Logist Regress Anal 58–79

  • Meten M, Bhandary NP, Yatabe R (2015) GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia. J Mt Sci 12(6):1355–1372

    Article  Google Scholar 

  • Meusburger K, Alewell C (2008) Impacts of anthropogenic and environmental factors on the occurrence of shallow landslides in an alpine catchment (Urseren Valley, Switzerland). Nat Hazard 8:509–520

    Article  Google Scholar 

  • Michael EA, Samanta S (2016) Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques. Model Earth Syst Environ 2(2):88

    Article  Google Scholar 

  • Mishra B, Babel MS, Tripathi NK (2014) Analysis of climatic variability and snow cover in the Kaligandaki River Basin, Himalaya, Nepal. Theor Appl Climatol 116(3–4):681–694

    Article  Google Scholar 

  • MoHA DN, UNDP O (2009) Nepal disaster report: the hazardscape and vulnerability

  • Moosavi V, Niazi Y (2016) Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping. Landslides 13(1):97–114

    Article  Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191

    Article  Google Scholar 

  • Neuhäuser B, Damm B, Terhorst B (2012) GIS-based assessment of landslide susceptibility on the base of the weights-of-evidence model. Landslides 9(4):511–528

    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(5):1443–1464

    Article  Google Scholar 

  • Parteli EJR, Gomes MAF, Brito VP (2005) Nontrivial temporal scaling in a Galilean stick-slip dynamics. Phys Rev E 71(3):036137

    Article  Google Scholar 

  • Petley DN, Hearn GJ, Hart A, Rosser NJ, Dunning SA, Oven K, Mitchell WA (2007) Trends in landslide occurrence in Nepal. Nat Hazards 43(1):23–44

    Article  Google Scholar 

  • Pham BT, Jaafari A, Prakash I, Bui DT (2019) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull Eng Geol Environ 78(4):2865–2886

    Article  Google Scholar 

  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250

    Article  Google Scholar 

  • Poudyal CP, Chang C, Oh HJ, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61(5):1049–1064

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: Which algorithm, which precision? CATENA 162:177–192

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34(3):216–235

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Pourghasemi HR, DhitaL MR, Pradhan B (2014) Landslide susceptibility mapping along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5):1266–1285

    Article  Google Scholar 

  • Remondo J, González A, De Terán JRD, Cendrero A, Fabbri A, Chung CJF (2003) Validation of landslide susceptibility maps; examples and applications from a case study in Northern Spain. Nat Hazards 30(3):437–449

    Article  Google Scholar 

  • Salciarini D, Fanelli G, Tamagnini C (2017) A probabilistic model for rainfall—induced shallow landslide prediction at the regional scale. Landslides 14(5):1731–1746

    Article  Google Scholar 

  • Salvatici T, Tofani V, Rossi G, D’Ambrosio M, Stefanelli CT, Masi EB, Catani F (2018) Application of a physically based model to forecast shallow landslides at a regional scale. Nat Hazard 18(7):1919–1935

    Article  Google Scholar 

  • Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5(1):1–15

    Article  Google Scholar 

  • Shrestha AB, Joshi SP (2009) Snow cover and glacier change study in Nepalese Himalaya using remote sensing and geographic information system. J Hydrol Meteorol 6(1):26–36

    Article  Google Scholar 

  • Swingler K (1996) Applying neural networks: a practical guide. Morgan Kaufmann

    Google Scholar 

  • Team RC (2019). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Math Probl Eng 2012

  • Upreti BN (1999) An overview of the stratigraphy and tectonics of the Nepal Himalaya. J Asian Earth Sci 17(5–6):577–606

    Article  Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114

    Article  Google Scholar 

  • Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76(3–4):392–410

    Article  Google Scholar 

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102(3–4):112–131

    Article  Google Scholar 

  • Westen CV, Terlien MJT (1996) An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth Surf Process Landf 21(9):853–868

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Xu C, Xu X, Dai F, Wu Z, He H, Shi F, Xu S (2013) Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Nat Hazards 68(2):883–900

    Article  Google Scholar 

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35(6):1125–1138

    Article  Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Asian Development Bank-Japanese Scholarship Program (ADB-JSP) for providing the partial financial support for the study. The authors are sincerely grateful for the valuable suggestions and inputs from Mr. Andang Soma Suriyana (PhD) and Mr. Aril Aditian (Ph.D.), the then members of Laboratory of Forest Conservation and Erosion Control, Department of Agro-environmental Sciences, Kyushu University, and Dr. Prem Prasad Paudel of Department of Forest and Soil Conservation, Nepal. The authors cordially thank Prachanda Gautam for assisting field data collection, and Mayumi Sato and Roma for the corrections on English language. The authors are indebted for the support of Mr. Shashank Sharma of Zoological Society of London-Nepal and anonymous reviewer for their constructive comments in shaping this manuscript.

Funding

This work is partially supported by Asian Development Bank-Japanese Scholarship Program.

Author information

Author notes

  1. Pawan Gautam

    Present address: Zoological Society of London-Nepal, Gulaf Marg, Bhatbhateni, PO Box 5867, Kathmandu, Nepal

Authors and Affiliations

  1. Laboratory of Forest Conservation and Erosion Control, Agro-Environmental Science Department, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka, 819-0395, Japan

    Pawan Gautam

  2. Faculty of Agriculture, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka, 819-0395, Japan

    Tetsuya Kubota

  3. RECOFTC, Bangkok, Thailand

    Lok Mani Sapkota

  4. Faculty of Agriculture, University of Miyazaki, 1-1 Gakuen-Kibanadai-Nishi, Miyazaki, 889-2192, Japan

    Yoshinori Shinohara

Authors
  1. Pawan Gautam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tetsuya Kubota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lok Mani Sapkota
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yoshinori Shinohara
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed for this paper.

Corresponding author

Correspondence to Pawan Gautam.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethics approval

NA.

Consent to participate

NA.

Consent for publication

We provide our consent to publish the submitted manuscript.

Additional information

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 23 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gautam, P., Kubota, T., Sapkota, L.M. et al. Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods. Environ Earth Sci 80, 359 (2021). https://doi.org/10.1007/s12665-021-09650-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-021-09650-2

Keywords

Search

Navigation