Abstract
The modeling and prediction of land movement susceptibility hazards, i.e., debris flow, landslide, and rock fall, can assist in controlling and preventing a variety of societal and environmental damages. The purpose of this study was to develop a land movement susceptibility hazard model of debris flow, landslide, and multiple land movement, i.e., combination of debris flow and landslide in the Saveh city of Markazi Province, Iran, using an ensemble of Bayesian generalized linear model (BGLM), sparse partial least squares (SPLS), boosted tree (BT), and random forest (RF) algorithms. For this purpose, 167 debris flow points, 261 landslide points, and 257 multiple (debris flow and landslide) points were identified based on field visits and available information, and 15 suitable conditioning factors were prepared as independent variables for this study. The accuracy and efficiency of the models were assessed using the receiver operating characteristic (ROC) and other statistical indices. The variable importance result indicates that slope is the most important factor in debris flow (25.53), landslide (31.39), and multiple hazard (41.90) occurrences. The accuracy assessment results in the validation phase revealed that the RF is the most optimal among the applied algorithms, with area under the curve (AUC) values of 0.90, 0.94, and 0.89 for debris flow, landslide, and multiple (D + L) hazard modeling. The findings of this study indicated that the use of a Bayesian and tree-based ensemble model in preparing a land movement-related disaster map could be useful among policymakers and land use planners for sustainable land use management and practices.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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 318:101–111
Aghda SMF, Bagheri V, Razifard M (2018) Landslide susceptibility mapping using fuzzy logic system and its influences on mainlines in lashgarak region, Tehran, Iran. Geotech Geol Eng 36:915–937
Al-Abadi AM, Shahid S (2015) A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region. Environ Monit Assess 187:576
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Env 58:21–44
Anagnostopoulos GG, Fatichi S, Burlando P (2015) An advanced process-based distributed model for the investigation of rainfall-induced landslides: the effect of process representation and boundary conditions. Water Resour Res 51:7501–7523
Arabameri A, Karimi-Sangchini E, Pal SC et al (2020) Novel credal decision tree-based ensemble approaches for predicting the landslide susceptibility. Remote Sens 12:3389. https://doi.org/10.3390/rs12203389
Arabameri A, Pourghasemi HR, Yamani M (2017) Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci 76:832
Arabameri A, Pradhan B, Rezaei K, Lee C-W (2019) Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sens 11. https://doi.org/10.3390/rs11090999
Arabameri A, Rezaie F, Pal SC et al (2021) Modelling of piping collapses and gully headcut landforms: evaluating topographic variables from different types of DEM. Geosci Front 101230
Avand M, Janizadeh S, Naghibi SA et al (2019) A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water 11:2076
Bai S-B, Wang J, Lü G-N 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
Band SS, Janizadeh S, Chandra Pal S et al (2020a) 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
Band SS, Janizadeh S, Chandra Pal S et al (2020b) Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing 12:3568. https://doi.org/10.3390/rs12213568
Biswajeet P, Saied P (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for L and slide susceptibility mapping. Disaster Adv 3:26–34
Bloechl A, Braun B (2005) Economic assessment of landslide risks in the Swabian Alb, Germany? research framework and first results of homeowners’ and experts’ surveys
Breiman L (2001) Random Forests. Mach Learn 45:5–32
Bui DT, Ho T-C, Pradhan B et al (2016) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75:1101
Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann Ser B 62:23–27
Cao C, Wang Q, Chen J et al (2016) Landslide susceptibility mapping in vertical distribution law of precipitation area: case of the Xulong Hydropower station Reservoir, Southwestern China. Water 8:270
Chakrabortty R, Pal SC, Chowdhuri I et al (2020) Assessing the importance of static and dynamic causative factors on erosion potentiality using SWAT, EBF with uncertainty and plausibility, logistic regression and novel ensemble model in a sub-tropical environment. J Indian Soc Remote Sens 1–25
Chen W, Chai H, Zhao Z et al (2016) Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County, China. Environ Earth Sci 75:474
Chen W, Hong H, Panahi M et al (2019a) Spatial prediction of landslide susceptibility using GIS-based data mining techniques of ANFIS with whale optimization algorithm (WOA) and grey wolf optimizer (GWO). Appl Sci 9:3755
Chen W, Li W, Hou E et al (2015) Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China. Arab J Geosci 8:1829–1841
Chen W, Pourghasemi HR, Naghibi SA (2018) Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bull Eng Geol Env 77:611–629
Chen W, Shirzadi A, Shahabi H et al (2017a) A novel hybrid artificial intelligence approach based on the rotation forest ensemble and na{\"\i}ve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomat Nat Haz Risk 8:1955–1977
Chen W, Xie X, Wang J et al (2017b) 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
Chen W, Yan X, Zhao Z et al (2019b) Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China). Bull Eng Geol Env 78:247–266
Chen X-W, Liu M (2005) Prediction of protein–protein interactions using random decision forest framework. Bioinformatics 21:4394–4400
Chen Y, Chen W, Chandra Pal S et al (2021) Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto Int 1–21
Chowdhuri I, Pal S, Arabameri A et al (2020) Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environ Earth Sci 79. https://doi.org/10.1007/s12665-020-09227-5
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 1–26
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 Environ 1–22
Chung CF (1998) Three Bayesian prediction models for landslide hazard. In: Proceedings of International Association for Mathematical Geology 1998 Annual Meeting
Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364
Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). CATENA 113:236–250
Dai FC, Lee CF, Li J, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40:381–391
De Jong S (1993) SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst 18:251–263
Deb SK, El-Kadi AI (2009) Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events. Geomorphology 108:219–233
Denuit M, Hainaut D, Trufin J (2019) Bayesian Neural Networks and GLM. In: Effective Statistical Learning Methods for Actuaries III. Springer, pp 43–61
Di Napoli M, Carotenuto F, Cevasco A et al (2020) Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides 17:1897–1914
Dietrich WE, Bellugi D, De Asua RR (2001) Validation of the shallow landslide model, SHALSTAB, for forest management. Water Science and Application 2:195–227
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 32:619–639
Dowling CA, Santi PM (2014) Debris flows and their toll on human life: a global analysis of debris-flow fatalities from 1950 to 2011. Nat Hazards 71:203–227
Feizizadeh B, Blaschke T (2011) Landslide risk assessment based on GIS multi-criteria evaluation: a case study in Bostan-Abad County, Iran. Journal of Earth Science and Engineering 1:66–77
Fell R, Wan CF, Cyganiewicz J, Foster M (2003) Time for development of internal erosion and piping in embankment dams. J Geotech Geoenviron Eng 129:307–314
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 1189–1232
Fuentes J, Poncela P, Rodr\’\iguez J (2015) Sparse partial least squares in time series for macroeconomic forecasting. J Appl Economet 30:576–595
Ghasemain B, Asl DT, Pham BT et al (2020) Shallow landslide susceptibility mapping: a comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam Journal of Earth Sciences
Glade T (2003) Landslide occurrence as a response to land use change: a review of evidence from New Zealand. CATENA 51:297–314
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
Golkarian A, Naghibi SA, Kalantar B, Pradhan B (2018) Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS. Environ Monit Assess 190:149
Gómez-Gutiérrez Á, Conoscenti C, Angileri SE et al (2015) Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations. Nat Hazards 79:291–314. https://doi.org/10.1007/s11069-015-1703-0
Gounaridis D, Chorianopoulos I, Symeonakis E, Koukoulas S (2019) A random forest-cellular automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. Sci Total Environ 646:320–335
Haftlang KK, Lang KKH (2003) The book of Iran: a survey of the geography of Iran. Alhoda UK
Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89:1255–1270
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media
Highland L, Bobrowsky PT others (2008) The landslide handbook: a guide to understanding landslides. US Geological Survey Reston
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
Iverson RM, Reid ME, LaHusen RG (1997) Debris-flow mobilization from landslides. Annu Rev Earth Planet Sci 25:85–138
Janizadeh S, Pal SC, Saha A et al (2021) Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. J Environ Manage 298:113551
Jebur MN, Pradhan B, Tehrany MS (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165
Kadavi PR, Lee C-W, Lee S (2018) Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens 10:1252
Kalantar B, Pradhan B, Naghibi SA 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). Geomat Nat Haz Risk 9:49–69
Kalantar B, Ueda N, Al-Najjar HAH et al (2019) An evaluation of landslide susceptibility mapping using remote sensing data and machine learning algorithms in Iran. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 4
Kalantar B, Ueda N, Saeidi V et al (2020) Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data. Remote Sens 12:1737
Kariminejad N, Hosseinalizadeh M, Pourghasemi HR et al (2019) Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model: Golestan Province, NE Iran. Sci Total Environ 677:281–298
Kritikos T, Davies T (2015) Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand. Landslides 12:1051–1075
Lee S, Kim J-C, Jung H-S et al (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomat Nat Haz Risk 8:1185–1203
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113
Li Y, Chen J, Zhang Y et al (2020) Debris flow susceptibility assessment and runout prediction: a case study in Shiyang Gully, Beijing, China. Int J Environ Res 14:365–383
Masetic Z, Subasi A (2016) Congestive heart failure detection using random forest classifier. Comput Methods Programs Biomed 130:54–64
Montgomery DC, Runger GC, Hubele NF (2009) Engineering statistics. John Wiley & Sons
Montgomery DR, Brandon MT (2002) Topographic controls on erosion rates in tectonically active mountain ranges. Earth Planet Sci Lett 201:481–489
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30
Moradi HR, Avand MT, Janizadeh S (2019) landslide susceptibility survey using modeling methods. In: Elsevier. pp 259–276
Nadim F, Kjekstad O, Peduzzi P et al (2006) Global landslide and avalanche hotspots. Landslides 3:159–173
Ngo PTT, Panahi M, Khosravi K et al (2021) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geosci Front 12:505–519
Nikolopoulos EI, Anagnostou EN, Borga M et al (2011) Sensitivity of a mountain basin flash flood to initial wetness condition and rainfall variability. J Hydrol 402:165–178
Nouraliee J, Porkhial S, Mohammadzadeh Moghaddam M et al (2015) Investigation of density contrasts and geologic structures of hot springs in the Markazi Province of Iran using the gravity method. Russ Geol Geophys 56:1791–1800. https://doi.org/10.1016/j.rgg.2015.11.011
Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343
Ozdemir A (2011) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136
Pal M, Maity R (2019) Development of a spatially-varying statistical soil moisture profile model by coupling memory and forcing using hydrologic soil groups. J Hydrol 570:141–155
Pal S, Chowdhuri I (2019) GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Appl Sci 1:416. https://doi.org/10.1007/s42452-019-0422-7
Pal S, Das B, Malik S (2019) Potential landslide vulnerability zonation using integrated analytic hierarchy process and GIS technique of upper rangit catchment area, west sikkim, India. J Indian Soc Remote Sens 47. https://doi.org/10.1007/s12524-019-01009-2
Pal SC, Arabameri A, Blaschke T et al (2020) Ensemble of machine-learning methods for predicting gully erosion susceptibility. Remote Sens 12:3675. https://doi.org/10.3390/rs12223675
Pal SC, Chakrabortty R, Roy P et al (2021) Changing climate and land use of 21st century influences soil erosion in India. Gondwana Res 94:164–185. https://doi.org/10.1016/j.gr.2021.02.021
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464
Persichillo MG, Bordoni M, Meisina C (2017) The role of land use changes in the distribution of shallow landslides. Sci Total Environ 574:924–937
Pham BT (2018) A novel classifier based on composite hyper-cubes on iterated random projections for assessment of landslide susceptibility. J Geol Soc India 91:355–362
Pham BT, Khosravi K, Prakash I (2017) Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India. Environmental Processes 4:711–730
Pourghasemi HR, Jirandeh AG, Pradhan B et al (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science 122:349–369
Pourghasemi HR, Kariminejad N, Amiri M et al (2020) Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Sci Rep 10:1–11
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75:185
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
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. Theoret Appl Climatol 130:609–633
Pourghasemi HR, Yousefi S, Kornejady A, Cerdà A (2017) Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci Total Environ 609:764–775
Pradhan B, Mansor S, Pirasteh S, Buchroithner MF (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32:4075–4087
Roy P, Chakrabortty R, Chowdhuri I et al (2020) Development of different machine learning ensemble classifier for gully erosion susceptibility in Gandheswari Watershed of West Bengal, India. In: Machine Learning for Intelligent Decision Science. Springer, pp 1–26
Saha A, Pal SC, Arabameri A et al (2021a) Flood susceptibility assessment using novel ensemble of hyperpipes and support vector regression algorithms. Water 13:241. https://doi.org/10.3390/w13020241
Saha A, Pal SC, Arabameri A et al (2021b) Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements. J Environ Manage 287:112284. https://doi.org/10.1016/j.jenvman.2021.112284
Saha A, Pal SC, Santosh M et al (2021c) Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: the present and future scenarios. J Clean Prod 128713
Santo A, Di Crescenzo G, Forte G et al (2018) Flow-type landslides in pyroclastic soils on flysch bedrock in southern Italy: the Bosco de’Preti case study. Landslides 15:63–82
Shafizadeh-Moghadam H, Minaei M, Shahabi H, Hagenauer J (2019) Big data in geohazard; pattern mining and large scale analysis of landslides in Iran. Earth Sci Inf 12:1–17
Simoni S, Zanotti F, Bertoldi G, Rigon R (2008) Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS. Hydrological Processes: an International Journal 22:532–545
Starkel L (1976) The role of extreme (catastrophic) meteorological events in contemporary evolution of slopes. Geomorphology and Climate 203–246
Sun X, Chen J, Bao Y et al (2018) Landslide susceptibility mapping using logistic regression analysis along the jinsha river and its tributaries close to Derong and Deqin County, Southwestern China. ISPRS Int J Geo Inf 7:438
Walley RJ, Smith CL, Gale JD, Woodward P (2015) Advantages of a wholly Bayesian approach to assessing efficacy in early drug development: a case study. Pharm Stat 14:205–215
Wang B, Waters C, Orgill S et al (2018) Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecol Ind 88:425–438
Wang Z-Y, Lee JHW, Melching CS (2015) Debris flows and landslides. In: Wang Z-Y, Lee JHW, Melching CS (eds) River Dynamics and Integrated River Management. Springer, Berlin, Heidelberg, pp 193–264
Wilson JP, Gallant JC (2000) Terrain analysis; principles and applications, Jhon Wiley&Sons. Inc USA
Wu Y, Li W, Wang Q et al (2016) Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arab J Geosci 9:84
Xiong K, Adhikari BR, Stamatopoulos CA et al (2020) Comparison of different machine learning methods for debris flow susceptibility mapping: a case study in the Sichuan Province, China. Remote Sens 12:295
Yahav I, Shmueli G, Mani D (2015) A tree-based approach for addressing self-selection in impact studies with big data. Yahav, I, Shmueli, G, & Mani, D(2016) A tree-based approach for addressing self-selection in impact studies with big data MIS Quarterly 40:819–848
Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41:201–226
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 35:1125–1138
Yousefi S, Pourghasemi HR, Emami SN et al (2020) A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci Rep 10:1–14
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13:839–856
Zhang K, Wu X, Niu R et al (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ Earth Sci 76:405
Zhang Y, Ge T, Tian W, Liou Y-A (2019) Debris flow susceptibility mapping using machine-learning techniques in shigatse area, china. Remote Sens 11:2801
Zhao Y, Staudenmayer J, Coull BA, Wand MP (2006) General design Bayesian generalized linear mixed models. Stat Sci 35–51
Zhou J, Cui P, Yang X (2013) Dynamic process analysis for the initiation and movement of the Donghekou landslide-debris flow triggered by the Wenchuan earthquake. J Asian Earth Sci 76:70–84
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pal, S.C., Chakrabortty, R., Saha, A. et al. Evaluation of debris flow and landslide hazards using ensemble framework of Bayesian- and tree-based models. Bull Eng Geol Environ 81, 55 (2022). https://doi.org/10.1007/s10064-021-02546-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10064-021-02546-2