Skip to main content

Advertisement

SpringerLink
Log in

Mapping of earthquake hotspot and coldspot zones for identifying potential landslide hotspot areas in the Himalayan region

  • Original Paper
  • Published:
Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Landslide and earthquake are two of the dangerous natural hazards in the Himalayan mountainous ranges. This study focused on small-scale landslide susceptibility (LS) maps as well as hotspot zones of earthquake and landslide on the Himalayas. Remote sensing, geological, and hydrological data were collected and processed using the geographic information system (GIS) for this purpose. Seventeen landslide influential causative factors (LICFs) were used, which included slope, aspect, elevation, profile curvature, plan curvature, topographic wetness index (TWI), stream power index (SPI), terrain ruggedness index (TRI), drainage density, distance to drainage, seismic zone, soil texture, geology, and land use and land cover (LULC). The machine learning (ML) neural network and deep learning models, namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), deep learning neural network (DLNN), and Getis-Ord GI*, have been applied for landslide susceptibility (LS) maps, and earthquake and landslide hotspot maps respectively of the Himalayas. The LS maps were compared with the earthquake and landslide hotspot zones, and there is valid agreement among these. The results of the susceptibility mapping were validated by the confusion matrix table and sensitivity, specificity, accuracy, precession, F score, and kappa statistical indices. The validation result showed that all the landslide susceptibility models (LSMs) have a near 90% prediction rate. Among the four models, the DLNN models have the highest prediction accuracy followed by LDA, QDA, and ANN. The small-scale LS maps of the Himalayas may help planners and developers for macro-scale mitigation of landslide hazard and land use planning.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Abedini M, Ghasemian B, Shirzadi A et al (2019) A novel hybrid approach of Bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int 34:1427–1457. https://doi.org/10.1080/10106049.2018.1499820

    Article  Google Scholar 

  • Abuzied S, Yuan M, Ibrahim S et al (2016) Geospatial risk assessment of flash floods in Nuweiba area. Egypt J Arid Environ 133:54–72

    Article  Google Scholar 

  • Abuzied SM, Alrefaee HA (2019) Spatial prediction of landslide-susceptible zones in El-Qaá area, Egypt, using an integrated approach based on GIS statistical analysis. Bull Eng Geol Environ 78:2169–2195

    Article  Google Scholar 

  • Abuzied SM, Mansour BMH (2018) Geospatial hazard modeling for the delineation of flash flood-prone zones in Wadi Dahab basin. Egypt J Hydrol 21:180–206. https://doi.org/10.2166/hydro.2018.043

    Article  Google Scholar 

  • Abuzied SM, Pradhan B (2021) Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt. Georisk: Assess. Manage Risk Eng Syst Geohazards 15:221–246

    Google Scholar 

  • Ahmad R, Joshi MN (2010) Assessment of landslide susceptibility on land degradation processes in Chamoli and surrounding area using RS and GIS technique. Int Geoinf Res Dev J 1:1

    Google Scholar 

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

    Article  Google Scholar 

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

    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. https://doi.org/10.1016/j.enggeo.2018.02.020

    Article  Google Scholar 

  • Anderson TW (1958) An introduction to multivariate statistical analysis. (No. 04; QA278, A534.)

  • Arabameri A, Karimi-Sangchini E, Pal SC et al (2020a) Novel credal decision tree-based ensemble approaches for predicting the landslide susceptibility. Remote Sens 12:3389

    Article  Google Scholar 

  • Arabameri A, Pourghasemi HR (2019) 13 - spatial modeling of gully erosion using linear and quadratic discriminant analyses in GIS and R. In: Pourghasemi HR, Gokceoglu C (eds) Spatial modeling in GIS and R for earth and environmental sciences. Elsevier, pp 299–321

    Chapter  Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K et al (2020b) An ensemble model for landslide susceptibility mapping in a forested area. Geocarto Int 35:1680–1705

    Article  Google Scholar 

  • Arabameri A, Saha S, Roy J et al (2020c) Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed. Iran Remote Sens 12:475

    Article  Google Scholar 

  • Aronson JE, Liang T-P, MacCarthy RV (2005) Decision support systems and intelligent systems. Pearson Prentice-Hall Upper Saddle River, NJ, USA

  • Arora A, Arabameri A, Pandey M et al (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain. India Sci Total Environ 750:141565

    Article  Google Scholar 

  • Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Institute for Signal and Information Processing 18:1–8

    Google Scholar 

  • Balamurugan G, Ramesh V, Touthang M (2016) Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Nat Hazards 84:465–488. https://doi.org/10.1007/s11069-016-2434-6

    Article  Google Scholar 

  • Band SS, Janizadeh S, Chandra Pal S et al (2020) Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility. Sensors 20:5609. https://doi.org/10.3390/s20195609

    Article  Google Scholar 

  • Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany-Liptovský Mikuláš railway case study. Phys Chem Earth, Parts a/b/c 35:162–171

    Article  Google Scholar 

  • Bhandari RK (1987) Slope instability in the fragile Himalaya and strategy for development. Ninth Annual Lecture Indian Geotech. J.

  • Brabb EE (1985) Innovative approaches to landslide hazard and risk mapping. International Landslide Symposium Proceedings. Canada, Toronto, pp 17–22

    Google Scholar 

  • Bui DT, Tsangaratos P, Ngo P-TT et al (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038–1054. https://doi.org/10.1016/j.scitotenv.2019.02.422

    Article  Google Scholar 

  • Bui DT, Tsangaratos P, Nguyen V-T et al (2020) Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188:104426. https://doi.org/10.1016/j.catena.2019.104426

    Article  Google Scholar 

  • Bui DT, Tuan TA, Hoang N-D 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 14:447–458

    Article  Google Scholar 

  • Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7:411–423. https://doi.org/10.1007/s10346-010-0202-3

    Article  Google Scholar 

  • Chen W, Li W, Hou E et al (2014) Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji. China Arab J Geosci 7:4499–4511

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chen W, Shirzadi A, Shahabi H et al (2017b) A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics. Nat Hazards Risk 8:1955–1977. https://doi.org/10.1080/19475705.2017.1401560

    Article  Google Scholar 

  • Chen W, Xie X, Wang J et al (2017c) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160

    Article  Google Scholar 

  • Chen Y, Chen CM, Liu ZY et al (2015) The methodology function of CiteSpace mapping knowledge domains. Soc Stud Sci 33:242–253

    Google Scholar 

  • Chen Y-R, Chen J-W, Hsieh S-C, Ni P-N (2009) The application of remote sensing technology to the interpretation of land use for rainfall-induced landslides based on genetic algorithms and artificial neural networks. IEEE J Sel Top Appl Earth Obs Remote Sens 2:87–95

    Article  Google Scholar 

  • Chowdhuri I, Pal SC, Arabameri A et al (2020a) Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region. India Environ Earth Sci 79:1–28

    Google Scholar 

  • Chowdhuri I, Pal SC, Arabameri A et al (2020b) Implementation of artificial intelligence based ensemble models for gully erosion susceptibility assessment. Remote Sens 12:3620

    Article  Google Scholar 

  • Chowdhuri I, Pal SC, Chakrabortty R et al (2021a) Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya. Nat Hazards 107:697–722

    Article  Google Scholar 

  • Chowdhuri I, Pal SC, Chakrabortty R et al (2021b) Spatial prediction of landslide susceptibility using projected storm rainfall and land use in Himalayan region. Bull. Eng. Geol. 1–22

  • Chowdhuri I, Pal SC, Chakrabortty R (2020c) Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res 65:1466–1489

    Article  Google Scholar 

  • Chowdhuri I, Roy P, Chakrabortty R et al (2020d) Development of hybrid computational approaches for landslide susceptibility mapping using remotely sensed data in East Sikkim, India. In: Applied intelligent decision making in machine learning. CRC Press, pp 71–92

  • Das M, Das A, Momin S, Pandey R (2020) Mapping the effect of climate change on community livelihood vulnerability in the riparian region of Gangatic Plain. India Ecol 119:106815

    Article  Google Scholar 

  • Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7:197–387

    Article  Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR et al (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 65:135–165

    Article  Google Scholar 

  • Dikshit A, Sarkar R, Pradhan B et al (2020) Rainfall induced landslide studies in Indian Himalayan region: a critical review. Appl Sci 10:2466. https://doi.org/10.3390/app10072466

    Article  Google Scholar 

  • Dikshit A, Satyam DN (2018) Estimation of rainfall thresholds for landslide occurrences in Kalimpong. India Innov Infrastruct Solut 3:1–10

    Google Scholar 

  • DiPietro JA, Pogue KR (2004) Tectonostratigraphic subdivisions of the Himalaya: a view from the west. Tectonics 23

  • Eker AM, Dikmen M, Cambazoğlu S et al (2015) Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey. Int J Geogr Inf Sci 29:132–158

    Article  Google Scholar 

  • Elkadiri R, Sultan M, Youssef AM et al (2014) A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sens 7:4818–4835

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C, Van Asch TW (2004) Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32:1–23

    Article  Google Scholar 

  • Erener A, Düzgün HSB (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fan X, Scaringi G, Korup O et al (2019) Earthquake-induced chains of geologic hazards: patterns, mechanisms, and impacts. Rev Geophys 57:421–503

    Article  Google Scholar 

  • Feizizadeh B, Shadman Roodposhti M, Jankowski P, Blaschke T (2014) A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci 73:208–221. https://doi.org/10.1016/j.cageo.2014.08.001

    Article  Google Scholar 

  • Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7:179–188

    Article  Google Scholar 

  • Gansser A (1964) Geology of the Himalayas

  • Getis A, ORD J (1992) The analysis of spatial association by use of distance statistics Geographical Analysis 24 (3): 189–206

  • Ghosh S, Carranza EJM, van Westen CJ et al (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131:35–56. https://doi.org/10.1016/j.geomorph.2011.04.019

    Article  Google Scholar 

  • Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78:11–27

    Article  Google Scholar 

  • Goodfellow I, Bengio Y, Courville A, Bach F (2017) Deep learning, illustrated edn. MIT Press, Cambridge, Massachusetts

    Google Scholar 

  • Graupe D (2016) Deep learning neural networks: design and case studies. World Scientific Publishing Company

  • Guzzetti F, Mondini AC, Cardinali M et al (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M et al (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299

    Article  Google Scholar 

  • Hassanzadeh R, Nedovic-Budic Z (2013) Identification of earthquake disaster hot spots with crowd sourced data. In: Intelligent systems for crisis management. Springer, pp 97–119

  • He Q, Xu Z, Li S et al (2019) Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy 21:106

    Article  Google Scholar 

  • Hong H, Liu J, Zhu A-X (2020) Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci Total Environ 718:137231

    Article  Google Scholar 

  • Hong H, Naghibi SA, Dashtpagerdi MM et al (2017) A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci 10:167

    Article  Google Scholar 

  • Howland P, Jeon M, Park H (2003) Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM J Matrix Anal Appl 25:165–179

    Article  Google Scholar 

  • Islam ARMT, Saha A, Ghose B et al (2021) Landslide susceptibility modeling in a complex mountainous region of Sikkim Himalaya using new hybrid data mining approach. Geocarto Int 1–26

  • Jana M, Sar N (2016) Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level. India Mmodel Earth Syst Environ 2:60

    Article  Google Scholar 

  • Janizadeh S, Avand M, Jaafari A et al (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the tafresh watershed. Iran Sustainability 11:5426

    Article  Google Scholar 

  • Jia L, Zheng X, Miao J (2018) Research progress and hotspot analysis of spatial interpolation. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing, p 012079

  • Kamp U, Growley BJ, Khattak GA, Owen LA (2008) GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology 101:631–642

    Article  Google Scholar 

  • Kanungo DP, Sharma S (2014) Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India. Landslides 11:629–638

    Article  Google Scholar 

  • Kargel JS, Leonard GJ, Shugar DH et al (2016) Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake. Science 351

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

    Article  Google Scholar 

  • Khanna K, Martha TR, Roy P, Kumar KV (2021) Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling. Landslides 18:2281–2294. https://doi.org/10.1007/s10346-021-01627-3

    Article  Google Scholar 

  • Khattri KM, Tyagi AK (1983) Seismicity patterns in the Himalayan plate boundary and identification of the areas of high seismic potential. Tectonophysics 96:281–297

    Article  Google Scholar 

  • Kim P (2017) Deep Learning. In: Kim P (ed) MATLAB deep learning: with machine learning, neural networks and artificial intelligence. Apress, Berkeley, CA, pp 103–120

    Chapter  Google Scholar 

  • Kumar A (2011) Modelling of stream flow and sediment delivery characteristics of Gangotri glacier basin Himalayas

  • Kumar S, Gupta V (2021) Evaluation of spatial probability of landslides using bivariate and multivariate approaches in the Goriganga valley, Kumaun Himalaya, India. Nat Hazards 109:2461–2488. https://doi.org/10.1007/s11069-021-04928-x

    Article  Google Scholar 

  • Lee J-H, Sameen MI, Pradhan B, Park H-J (2018) Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods. Geomorphology 303:284–298

    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

    Article  Google Scholar 

  • Lee S, Ryu J-H, Won J-S, Park H-J (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Lewis ND (2016) Deep learning made easy with R: a gentle introduction for data science. CreateSpace Independent Publishing Platform, Place of publication not identified

    Google Scholar 

  • Lu P, Bai S, Tofani V, Casagli N (2019) Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS J Photogramm 156:147–159

    Article  Google Scholar 

  • Luna T, Rocha A, Carvalho AC et al (2011) Modelling the extreme precipitation event over Madeira Island on 20 February 2010. Nat Hazards Earth Syst 11

  • Manepalli UR, Bham GH, Kandada S (2011) Evaluation of hotspots identification using kernel density estimation (K) and Getis-Ord (Gi*) on I-630. In: 3rd International Conference on Road Safety and Simulation. pp 14–16

  • Mathew J, Jha VK, Rawat GS (2007) Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India. Int J Remote Sens 28:2257–2275. https://doi.org/10.1080/01431160600928583

    Article  Google Scholar 

  • McLachlan GJ (2004) Discriminant analysis and statistical pattern recognition. John Wiley & Sons

    Google Scholar 

  • Molnar P (1984) Structure and tectonics of the Himalaya: constraints and implications of geophysical data. Annu Rev Earth Planet Sci 12:489–516

    Article  Google Scholar 

  • Naghibi SA, Dashtpagerdi MM (2017) Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeol J 25:169–189

    Article  Google Scholar 

  • Naithani AK (1999) The Himalayan Landslides Employment News 23:20–26

    Google Scholar 

  • Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 20:77–90. https://doi.org/10.1007/s11269-006-4007-z

    Article  Google Scholar 

  • Nsengiyumva JB, Luo G, Amanambu AC et al (2019) Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Sci Total Environ 659:1457–1472

    Article  Google Scholar 

  • Oh H-J, Lee S (2017) Shallow landslide susceptibility modeling using the data mining models artificial neural network and boosted tree. Appl Sci 7:1000. https://doi.org/10.3390/app7101000

    Article  Google Scholar 

  • Onagh M, Kumra VK, Rai PK (2012) Landslide susceptibility mapping in a part of Uttarkashi district (India) by multiple linear regression method. International Journal of Geology. Environ Earth Sci 2:102–120

    Google Scholar 

  • Pal SC, Chowdhuri I (2019) GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim. India SN Appl 1:416

    Article  Google Scholar 

  • Pan B, Shi Z, Xu X (2018) MugNet: Deep learning for hyperspectral image classification using limited samples. ISPRS J Photogramm 145:108–119

    Article  Google Scholar 

  • Pandey VK, Sharma KK, Pourghasemi HR, Bandooni SK (2019) Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh). India Catena 182:104150. https://doi.org/10.1016/j.catena.2019.104150

    Article  Google Scholar 

  • Pareek N, Sharma ML, Arora MK (2010) Impact of seismic factors on landslide susceptibility zonation: a case study in part of Indian Himalayas. Landslides 7:191–201. https://doi.org/10.1007/s10346-009-0192-1

    Article  Google Scholar 

  • Park S, Hamm S-Y, Kim J (2019) Performance evaluation of the GIS-based data-mining techniques decision tree, random forest, and rotation forest for landslide susceptibility modeling. Sustainability 11:5659

    Article  Google Scholar 

  • Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63

    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 78:2865–2886. https://doi.org/10.1007/s10064-018-1281-y

    Article  Google Scholar 

  • Pham BT, Phong TV, Nguyen-Thoi T et al (2020) GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv Space Res 66:1303–1320. https://doi.org/10.1016/j.asr.2020.05.016

    Article  Google Scholar 

  • Pham BT, Pradhan B, Bui DT 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

    Article  Google Scholar 

  • Polyansky OP (2002) Dynamic causes for the opening of the Baikal Rift Zone: a numerical modelling approach. Tectonophysics 351:91–117

    Article  Google Scholar 

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

    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 63:965–996

    Article  Google Scholar 

  • Pourghasemi HR, Rossi M (2017) Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol 130:609–633

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054

    Article  Google Scholar 

  • Pradhan B, Singh RP, Buchroithner MF (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Adv Space Res 37:698–709

    Article  Google Scholar 

  • Prasannakumar V, Vijith H, Charutha R, Geetha N (2011) Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Environ Sci 21:317–325

    Google Scholar 

  • Rahmati O, Falah F, Dayal KS et al (2020) Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Sci Total Environ 699:134230

    Article  Google Scholar 

  • Rawat JS, Joshi RC (2012) Remote-sensing and GIS-based landslide-susceptibility zonation using the landslide index method in Igo River Basin, Eastern Himalaya, India. Int J Remote Sens 33:3751–3767. https://doi.org/10.1080/01431161.2011.633121

    Article  Google Scholar 

  • Razavizadeh S, Solaimani K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76:1–16

    Article  Google Scholar 

  • Roy P, Chandra Pal S, Arabameri A et al (2020) Novel ensemble of multivariate adaptive regression spline with spatial logistic regression and boosted regression tree for gully erosion susceptibility. Remote Sens 12:3284

    Article  Google Scholar 

  • Sah MP, Mazari RK (1998) Anthropogenically accelerated mass movement, Kulu Valley, Himachal Pradesh, India. Geomorphology 26:123–138

    Article  Google Scholar 

  • Saha A, Pal SC, Santosh M et al (2021) Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: the present and future scenarios. J Clean Prod 128713

  • Saha AK, Gupta RP, Sarkar I et al (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2:61–69. https://doi.org/10.1007/s10346-004-0039-8

    Article  Google Scholar 

  • Sahin EK (2020) Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto Int 1–25

  • Sarkar S, Kanungo DP, Mehrotra GS (1995) Landslide hazard zonation: a case study in Garhwal Himalaya, India. Mt Res Dev 301–309

  • Sarkar S, Kanungo DP, Patra AK, Kumar P (2012) GIS based landslide susceptibility mapping–a case study in Indian Himalaya

  • Seber GA (2009) Multivariate observations. John Wiley & Sons

    Google Scholar 

  • Shrestha S, Kang T-S (2019) Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake. Nepal Bull Eng Geol Environ 78:1829–1842. https://doi.org/10.1007/s10064-017-1191-4

    Article  Google Scholar 

  • Shroder JF Jr, Bishop MP (1998) Mass movement in the Himalaya: new insights and research directions. Geomorphology 26:13–35

    Article  Google Scholar 

  • Sörensen R, Zinko U, Seibert J (2006) On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol Earth Syst Sci 10:101–112

    Article  Google Scholar 

  • Sudmeier-Rieux K, Jaquet S, Derron M-H et al (2012) A case study of coping strategies and landslides in two villages of Central-Eastern Nepal. Appl Geogr 32:680–690

    Article  Google Scholar 

  • Thai Pham B, Shirzadi A, Shahabi H et al (2019) Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability 11:4386

    Article  Google Scholar 

  • Tharwat A, Gaber T, Ibrahim A, Hassanien AE (2017) Linear discriminant analysis: a detailed tutorial. AI Commun 30:169–190

    Article  Google Scholar 

  • Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings. 1991 IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 586–587

  • Valdiya KS (1984) Evolution of the Himalaya. Tectonophysics 105:229–248

    Article  Google Scholar 

  • 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 188:104451

    Article  Google Scholar 

  • Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice

  • Wang G, Chen X, Chen W (2020) Spatial prediction of landslide susceptibility based on gis and discriminant functions. ISPRS Int J Geoinf 9:144

    Article  Google Scholar 

  • Wang J, Chen Y, Hao S et al (2019) Deep learning for sensor-based activity recognition: a survey. Pattern Recognit Lett 119:3–11. https://doi.org/10.1016/j.patrec.2018.02.010

    Article  Google Scholar 

  • Yang Z, Wang J, Zheng Z, Bai X (2018) A new method for recognizing cytokines based on feature combination and a support vector machine classifier. Molecules 23:2008. https://doi.org/10.3390/molecules23082008

    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

    Article  Google Scholar 

  • Ye J (2007) Least squares linear discriminant analysis. In: Proceedings of the 24th international conference on Machine learning. pp 1087–1093

  • Youssef AM, Pourghasemi HR (2020) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geosci Front 12:639–655

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India

    Indrajit Chowdhuri, Subodh Chandra Pal, Asish Saha, Rabin Chakrabortty & Paramita Roy

Authors
  1. Indrajit Chowdhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subodh Chandra Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asish Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rabin Chakrabortty
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paramita Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subodh Chandra Pal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chowdhuri, I., Pal, S.C., Saha, A. et al. Mapping of earthquake hotspot and coldspot zones for identifying potential landslide hotspot areas in the Himalayan region. Bull Eng Geol Environ 81, 257 (2022). https://doi.org/10.1007/s10064-022-02761-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10064-022-02761-5

Keywords

Search

Navigation