Abstract
Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models.
This is a preview of subscription content, log in via an institution to check access.
(Source: Field survey 2017, 2018)
(Source: Field survey 2017, 2018 and GSI reports)
Similar content being viewed by others
References
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. https://doi.org/10.1016/j.geomorph.2018.06.006
Aghdam IN, Varzandeh MHM, Pradhan B (2016) Landslide susceptibility mapping using a statistical ensemble index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci. https://doi.org/10.1007/s12665-015-5233-6
Alexoudi MN, Manolopoulou SB, Papaliangas THT (2010) A methodology for landslide risk assessment and management. J Environ Prot Ecol 77(7):1–12
Arabameri A, Pradhan B, Rezaei K et al (2019) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci. https://doi.org/10.1007/s11629-018-5168-y
Banerjee P, Ghose MK, Pradhan R (2018) Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya. Arab J Geosci 11:1–18. https://doi.org/10.1007/s12517-018-3488-4
Bera S, Guru B, Chatterjee R, Shaw R (2020a) Geographic variation of resilience to landslide hazard: a household-based comparative studies in Kalimpong hilly region, India. Int J Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2019.101456
Bera S, Guru B, Oommen T (2020b) Indicator-based approach for assigning physical vulnerability of the houses to landslide hazard in the Himalayan region of India. Int J Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2020.101891
Bera S, Guru B, Ramesh V (2019) Evaluation of landslide susceptibility models: a comparative study on Western Ghat Region, India. Remote Sens Appl Soc Environ 13:39–52. https://doi.org/10.1016/j.rsase.2018.10.010
Bouali EH, Oommen T, Escobar-Wolf R (2019) Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: a case study in the Portuguese bend landslide complex, California. Remote Sens. https://doi.org/10.3390/rs11080946
Brabb EE (1984) No Title. Innovative approaches to landslide hazard and risk mapping. BiTech Publishers, Vancouver, Toronto, Canada, pp 307–324
Bui DT, Shahabi H, Shirzadi A et al (2018) Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors (Switzerland). https://doi.org/10.3390/s18082464
Can A, Dagdelenler G, Ercanoglu M, Sonmez H (2019) Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1034-3
Chawla A, Pasupuleti S, Chawla S et al (2019) Landslide susceptibility zonation mapping: a case study from Darjeeling District, Eastern Himalayas, India. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-018-0916-6
Chen W, Xie X, Peng J et al (2018a) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA. https://doi.org/10.1016/j.catena.2018.01.012
Chen W, Zhang S, Li R, Shahabi H (2018b) Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ 644:1006–1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
Conforti M, Pascale S, Sdao F (2015) Mass movements inventory map of the Rubbio stream catchment (Basilicata—South Italy). J Maps 11:454–463. https://doi.org/10.1080/17445647.2014.924038
Corominas J, van Westen C, Frattini P et al (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-013-0538-8
Cruden DM, Varnes DJ (1996) Landslide types and processes special report—national research council. Transp Res Board 2:329–342
Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125. https://doi.org/10.1016/j.geomorph.2012.08.004
de Graff JV, Romesburg HC, Ahmad R, McCalpin JP (2012) Producing landslide-susceptibility maps for regional planning in data-scarce regions. Nat Hazards. https://doi.org/10.1007/s11069-012-0267-5
Devoli G, Strauch W, Chávez G, Høeg K (2007) A landslide database for Nicaragua: a tool for landslide-hazard management. Landslides 4:163–176. https://doi.org/10.1007/s10346-006-0074-8
Dou J, Yunus AP, Merghadi A et al (2020) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137320
Duo Z, Wang W, Wang H (2019) Oceanic mesoscale eddy detection method based on deep learning. Remote Sens 11(16):1921
Elmoulat M, Brahim LA (2018) Landslides susceptibility mapping using GIS and weights of evidence model in Tetouan-ras-Mazari area (Northern Morocco). Geomatics, Nat Hazards Risk. https://doi.org/10.1080/19475705.2018.1505666
Escobar-Wolf R, Sanders JD, Vishnu CL, Oommen T, Sajinkumar KS (2021) A GIS tool for infinite slope stability analysis (GIS-TISSA). Geosci Front 12(2):756–768
Fell R, Glastonbury J, Hunter G (2007) Rapid landslides: the importance of understanding mechanisms and rupture surface mechanics. Q J Eng Geol Hydrogeol. https://doi.org/10.1144/1470-9236/06-030
Finlay S (2012) Credit Scoring, response modeling, and insurance rating. Palgrave Macmillan UK, London
Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-18-2161-2018
Ghorbanzadeh O, Blaschke T, Gholamnia K et al (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. https://doi.org/10.3390/rs11020196
Ghosh S, Van Westen CJ, Carranza EJM et al (2009) A quantitative approach for improving the BIS (Indian) method of medium-scale landslide susceptibility. J Geol Soc India 74:625–638. https://doi.org/10.1007/s12594-009-0167-9
Ghosh S, van Westen CJ, Carranza EJM, Jetten VG (2012) Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India. Landslides 9:371–384. https://doi.org/10.1007/s10346-011-0304-6
Glade T, Crozier M (1996) Towards a national landslide information base for New Zealand. N Z Geogr. https://doi.org/10.1111/j.1745-7939.1996.tb00461.x
Gnyawali KR, Zhang Y, Wang G et al (2020) Mapping the susceptibility of rainfall and earthquake triggered landslides along China-Nepal highways. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-019-01583-2
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11. https://doi.org/10.1016/j.cageo.2015.04.007
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. https://doi.org/10.1007/s41324-017-0143-1
Guzzetti F, Mondini AC, Cardinali M et al (2012) Landslide inventory maps: new tools for an old problem. Earth-Science Rev 112:42–66
Hao L, Rajaneesh A, Van Westen C et al (2020) Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in for land use change analysis. Earth Syst Sci Data. https://doi.org/10.5194/essd-12-2899-2020
Hong H, Pradhan B, Xu C, Tien Bui D (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. https://doi.org/10.1016/j.catena.2015.05.019
Huang F, Zhang J, Zhou C et al (2020) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides. https://doi.org/10.1007/s10346-019-01274-9
Iwahashi J, Kamiya I, Yamagishi H (2012) High-resolution DEMs in the study of rainfall- and earthquake-induced landslides: use of a variable window size method in digital terrain analysis. Geomorphology 153–154:29–38. https://doi.org/10.1016/j.geomorph.2012.02.002
Jaiswal P, van Westen CJ, Jetten V (2010) Quantitative landslide hazard assessment along a transportation corridor in southern India. Eng Geol 116:236–250. https://doi.org/10.1016/j.enggeo.2010.09.005
Kalantar B, Pradhan B, Amir Naghibi S et al (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Nat Hazards Risk 9:49–69. https://doi.org/10.1080/19475705.2017.1407368
Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards 59:1491–1512. https://doi.org/10.1007/s11069-011-9847-z
Kern AN, Addison P, Oommen T et al (2017) Machine learning based predictive modeling of debris flow probability following wildfire in the Intermountain Western United States. Math Geosci. https://doi.org/10.1007/s11004-017-9681-2
Keyport RN, Oommen T, Martha TR et al (2018) A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images. Int J Appl Earth Obs Geoinf. https://doi.org/10.1016/j.jag.2017.08.015
Khosravi K, Shahabi H, Pham BT et al (2019) A comparative assessment of flood susceptibility modeling using Multi-criteria decision-making analysis and machine learning methods. J Hydrol. https://doi.org/10.1016/j.jhydrol.2019.03.073
Kuriakose SL, Devkota S, Rossiter DG, Jetten VG (2009) Prediction of soil depth using environmental variables in an anthropogenic landscape, a case study in the Western Ghats of Kerala, India. CATENA. https://doi.org/10.1016/j.catena.2009.05.005
Lee S (2019) Current and future status of GIS-based landslide susceptibility mapping a literature review. Korean J Remote Sens 35(1):179–193
Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol. https://doi.org/10.1007/s00254-006-0256-7
Lin Q, Wang Y (2018) Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides 15:2357–2372. https://doi.org/10.1007/s10346-018-1037-6
Liu Z, Gilbert G, Cepeda JM et al (2021) Modelling of shallow landslides with machine learning algorithms. Geosci Front. https://doi.org/10.1016/j.gsf.2020.04.014
Martha TR, Kerle N, Jetten V et al (2010) Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology. https://doi.org/10.1016/j.geomorph.2009.10.004
Melo R, Vieira G, Caselli A, Ramos M (2012) Susceptibility modelling of hummocky terrain distribution using the information value method (Deception Island, Antarctic Peninsula). Geomorphology. https://doi.org/10.1016/j.geomorph.2011.12.027
Melo R, Zêzere JL (2017) Modeling debris flow initiation and run-out in recently burned areas using data-driven methods. Nat Hazards 88:1373–1407. https://doi.org/10.1007/s11069-017-2921-4
Melo R, Zêzere JL, Rocha J, Oliveira SC (2019) Combining data-driven models to assess susceptibility of shallow slides failure and run-out. Landslides. https://doi.org/10.1007/s10346-019-01235-2
Merghadi A, Yunus AP, Dou J et al (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth-Sci Rev 207:103225
Mezaal MR, Pradhan B, Sameen MI et al (2017) Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data. Appl Sci. https://doi.org/10.3390/app7070730
Milevski I, Dragićević S, Zorn M (2019) Statistical and expert-based landslide susceptibility modeling on a national scale applied to North Macedonia. Open Geosci. https://doi.org/10.1515/geo-2019-0059
Mirdda HA, Bera S, Siddiqui MA, Singh B (2019) Analysis of bi-variate statistical and multi-criteria decision-making models in landslide susceptibility mapping in lower Mandakini Valley, India. GeoJ. https://doi.org/10.1007/s10708-019-09991-3
Mitra D, Bhandery C, Mukhopadhyay A et al (2018) Landslide Risk assessment in Darjeeling hills using multi-criteria decision support system: a Bayesian network approach. Disaster risk governance in India and cross cutting issues. Springer, Singapore, pp 361–386
Mukherjee A, Mitra A (2001) Geotechnical study of mass movements along the Kalimpong approach road in the Eastern Himalayas. Indian J Geol 73:271–280
Oommen T, Cobin PF, Gierke JS, Sajinkumar KS (2018) Significance of variable selection and scaling issues for probabilistic modeling of rainfall-induced landslide susceptibility. Spat Inf Res. https://doi.org/10.1007/s41324-017-0154-y
Pang S, Li TX, Zhang XF et al (2011) Spatial variability of cropland lead and its influencing factors: a case study in Shuangliu county, Sichuan province, China. Geoderma. https://doi.org/10.1016/j.geoderma.2011.01.002
Park S, Kim J (2019) Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl Sci. https://doi.org/10.3390/app9050942
Pathak D (2016) Knowledge based landslide susceptibility mapping in the Himalayas. Geoenviron Disasters. https://doi.org/10.1186/s40677-016-0042-0
Pham BT, Pradhan B, Tien Bui D et al (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. https://doi.org/10.1016/j.envsoft.2016.07.005
Pham BT, Prakash I, Tien Bui D (2018) Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 303:256–270. https://doi.org/10.1016/j.geomorph.2017.12.008
Pham BT, Tien Bui D, Pourghasemi HR et al (2017) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol. https://doi.org/10.1007/s00704-015-1702-9
Polykretis C, Chalkias C (2018) Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Nat Hazards. https://doi.org/10.1007/s11069-018-3299-7
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci. https://doi.org/10.1007/s12665-015-4950-1
Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA. https://doi.org/10.1016/j.catena.2017.11.022
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. https://doi.org/10.1007/s12665-014-3954-6
Ray PKC, Dimri S, Lakhera RC, Sati S (2007) Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides 4:101–111. https://doi.org/10.1007/s10346-006-0068-6
Roy J, Saha S, Arabameri A et al (2019) A novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong districts, West Bengal, India. Remote Sens. https://doi.org/10.3390/rs11232866
Saha A, Saha S (2020) Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-00783-1
Sajinkumar KS, Oommen T (2021) Landslide atlas of Kerala. GSI Publications 7(1):34
Sajinkumar KS, Rinu S, Oommen T, Vishnu CL, Praveen KR, Rani VR, Muraleedharan C (2020) Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach. Nat Hazards 103:639–657
Santangelo M, Gioia D, Cardinali M et al (2015) Landslide inventory map of the upper Sinni River valley, Southern Italy. J Maps 11:444–453. https://doi.org/10.1080/17445647.2014.949313
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. https://doi.org/10.1007/s12594-013-0162-z
Sevgen E, Kocaman S, Nefeslioglu HA, Gokceoglu C (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors. https://doi.org/10.3390/s19183940
Shirzadi A, Soliamani K, Habibnejhad M et al (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors. https://doi.org/10.3390/s18113777
Siddiqi N (2006) Credit risk scorecards developing and implementing intelligent credit scoring. Wiley
Silva RF, Marques R, Gaspar JL (2018) Implications of landslide typology and predisposing factor combinations for probabilistic landslide susceptibility models: a case study in Lajedo Parish (Flores Island, Azores—Portugal). Geosciences. https://doi.org/10.3390/geosciences8050153
Soja R, Starkel L (2007) Extreme rainfalls in Eastern Himalaya and southern slope of Meghalaya Plateau and their geomorphologic impacts. Geomorphology. https://doi.org/10.1016/j.geomorph.2006.01.040
Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using random forest. Big Earth Data. https://doi.org/10.1080/20964471.2018.1472392
Tanoli JI, Ningsheng C, Regmi AD, Jun L (2017) Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi, Nepal. Arab J Geosci. https://doi.org/10.1007/s12517-017-3026-9
Thi Ngo PT, Panahi M, Khosravi K et al (2020) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front. https://doi.org/10.1016/j.gsf.2020.06.013
Thiery Y, Malet JP, Sterlacchini S et al (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology. https://doi.org/10.1016/j.geomorph.2007.02.020
Tien Bui D, Tuan TA, Hoang ND et al (2017) Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides. https://doi.org/10.1007/s10346-016-0711-9
Tripathi R, Nayak AK, Shahid M et al (2015) Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging. Arab J Geosci. https://doi.org/10.1007/s12517-015-2003-4
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. CATENA. https://doi.org/10.1016/j.catena.2016.06.004
Tsou CY, Chigira M, Matsushi Y et al (2017) Coupling fluvial processes and landslide distribution toward geomorphological hazard assessment: a case study in a transient landscape in Japan. Landslides 14:1901–1914. https://doi.org/10.1007/s10346-017-0838-3
Van Dao D, Jaafari A, Bayat M et al (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena. https://doi.org/10.1016/j.catena.2019.104451
van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol. https://doi.org/10.1016/j.enggeo.2008.03.010
van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ. https://doi.org/10.1007/s10064-005-0023-0
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.scitotenv.2019.02.263
Weidner L, Oommen T, Escobar-Wolf R, Sajinkumar KS, Samuel RA (2018) Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions. Landslides 15(12):2343–2356
Weidner L, DePrekel K, Oommen T, Vitton S (2019) Investigating large landslides along a river valley using combined physical, statistical, and hydrologic modeling. Eng Geol 259:105169
Xiao L, Zhang Y, Peng G (2018) Landslide susceptibility assessment using integrated deep learning algorithm along the China-Nepal highway. Sensors. https://doi.org/10.3390/s18124436
Xu C, Xu X, Yao X, Dai F (2014) Three (nearly) complete inventories of landslides triggered by the May 12, 2008 Wenchuan MW 7.9 earthquake of China and their spatial distribution statistical analysis. Landslides 11:441–461. https://doi.org/10.1007/s10346-013-0404-6
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. https://doi.org/10.1016/j.cageo.2008.08.007
Yusof N, Ramli MF, Pirasteh S, Shafri HZM (2011) Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway, Malaysia. Int J Remote Sens. https://doi.org/10.1080/01431161.2010.484434
Zakharov AI, Zakharova LN, Krasnogorskii MG (2018) Monitoring landslide activity by radar interferometry using trihedral corner reflectors. Izv—Atmos Ocean Phys. https://doi.org/10.1134/S0001433818090451
Zêzere JL (2002) Landslide susceptibility assessment considering landslide typology. A case study in the area north of Lisbon (Portugal). Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-2-73-2002
Zêzere JL, Pereira S, Melo R et al (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2017.02.188
Acknowledgements
The research was carried out under the Junior Research Fellowship of the University Grant Commission (India) (UGC-Ref. No.: 3181/(NET-DEC. 2014)) and grants from the Digital Globe Foundation (Sales Order Number: 0057506095), the USA of the first author during his Ph.D. We are grateful to the Geological Survey of India (GSI), Save The Hill (NGO), government officers of Kalimpong, and above all, the local people of Kalimpong for their valuable help. The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions to enhance the manuscript content.
Funding
The first author (Somnath Bera) has received the fund to carry out the research from the following agencies University Grants Commission (UGC-Ref. No.: 3181/(NET-DEC. 2014)) DigitalGlobe Foundation (Sales Order Number: 0057506095).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bera, S., Upadhyay, V.K., Guru, B. et al. Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India. Nat Hazards 108, 1257–1289 (2021). https://doi.org/10.1007/s11069-021-04731-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-021-04731-8