Abstract
The purpose of this study is to develop a landslide susceptibility prediction model by applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part of Uttarakhand, India. First, a landslide inventory was carried out from the interpretation of satellite images. Thereafter, the landslide inventory points were randomly separated into training and validation datasets. Subsequently, the significant landslide causative factors such as slope, lithology, lineament density, land use/land cover, drainage density, aspect, elevation, road buffer, normalized differential vegetation index (NDVI), stream power index, and topographic wetness index were identified to run the model set up. Next, applying the FR statistical model in a GIS environment for development of landslide susceptibility index map and divided into five distinct landslide susceptibility zones (very low, low, moderate, high, and very high). To validate the results, the Receiver Operating Characteristics (ROC) curve were developed to check the accurrancy of the model, and it was observed that the prediction value of the FR model was reasonably accurate (86.1% at 95% confidence level). The output LSI map would be helpful for the government and planners to map and monitor potential landslide areas and mitigate the hazards.
Similar content being viewed by others
References
Abedini, M., Tulabi, S.: Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of nojian watershed in Lorestan province, Iran. Environ. Earth Sci. 77(11), 405 (2018)
Akgun, A.: A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at İzmir, Turky. Landslides 9(1), 93–106 (2012)
Al-Saady, Y., Merkel, B., Al-Tawash, B., Qusay Al-Suhail: Land use and land cover (LULC) mapping and change detection in the little Zab River Basin (LZRB), Kurdistan Region, NE Iraq and NW Iran. FOG-Freiberg Online Geoscience 43. (2015)
Bonham-Carter, G.F.: Geographic information systems for geoscientists-modeling with GIS. Comput. Methods Geosci. 13, 398 (1994)
Bui, D., Tien, B., Pradhan, O., Lofman, I., Revhaug, Oystein, B.D.: Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and bayesian regularized neural networks. Geomorphology. 171, 12–29 (2012)
Bui, D., Tien, B., Pradhan, I., Revhaug, D.B., Nguyen, H.V., Pham, Quy Ngoc Bui: A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics Nat. Hazards Risk. 6(3), 243–271 (2015)
Catani, F., Lagomarsino, D., Segoni, S., Tofani, V.: Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat. Hazards Earth Syst. Sci. 13(11), 2815 (2013)
Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Bui, D.T., Duan, Z., Li, S., A-Xing Zhu: GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena. 164, 135–149 (2018)
Dai, F.C., Lee, C.F.: Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology. 42(3–4), 213–228 (2002)
Dai, F.C., Lee, C.F., Li, J.X.Z.W., Xu, Z.W.: Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ. Geol. 40(3), 381–391 (2001)
Das, I., Stein, A., Kerle, N., Dadhwal, V.K.: Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India. Landslides 8(3), 293–308 (2011)
Deng, X., Li, L., Yufang Tan: Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity. ISPRS Int. J. Geo-Inf. 6(4), 103 (2017)
Dilley, M., Chen, R.S., Deichmann, U., Arthur, L., Lerner-Lam, and Margaret Arnold: Natural Disaster Hotspots: A Global risk Analysis. The World Bank (2005)
ESRI, A.: ArcGIS 10.1. Environmental Systems Research Institute, Redlands. (2012)
Fayez, L., Pazhman, D., Pham, B.T., Dholakia, M., Solanki, H., Khalid, M., Prakash, I.: Application of frequency ratio model for the development of Landslide susceptibility mapping at part of Uttarakhand State, India. Int. J. Appl. Eng. Res. 13, 6846–6854 (2018)
Froude, M.J., Petley, D.: Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci. 18, 2161–2181 (2018)
Gao, H., Fam, P.S., Tay, L.T., Low, H.C.: An overview and comparison on recent landslide susceptibility mapping methods. Disaster Adv. 12(12), 46–64 (2019)
Geomatica: II, and Geomatica OrthoEngine. “Geomatica I.“ (2004)
Gomez, H.: Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng. Geol. 78(1–2), 11–27 (2005)
Gorsevski, P.V., Brown, M.K., Panter, K., Onasch, C.M., Simic, A.: Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park Ohio. Landslides 13(3), 467–484 (2016)
Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P.: Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study Central Italy. Geomorphology 31(1–4), 181–216 (1999)
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F.: Probabilistic landslide hazard assessment at the basin scale. Geomorphology. 72(1–4), 272–299 (2005)
Hasegawa, S., Nonomura, A., Nakai, S., Dahal, R.K.: Drainage density as rainfall induced landslides susceptibility index in small catchment area. Int. J. Landslide Environ. 1(1), 27–28 (2014)
Hervás, J., Barredo, J.I., Rosin, P.L., Pasuto, A., Mantovani, F., Silvano, S.: Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide Italy. Geomorphology 54(1–2), 63–75 (2003)
Hong, H., Chen, W., Xu, C., Ahmed, M., Youssef, B., Pradhan, Dieu Tien Bui: Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int. 32(2), 139–154 (2017)
Hong, H., Pradhan, B., Xu, C., Dieu Tien Bui: 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 (2015)
Huang, F., Yao, C., Liu, W., Li, Y., Xiaowen Liu: Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine. Geomatics Nat. Hazards Risk 9(1), 919–938 (2018)
Inc, S.P.S.S.: SPSS 16.0 for Windows (Version 16.0)[Computer Software]. Author, Chicago (2007)
Kaur, H., Gupta, S., Parkash, S.: Comparative evaluation of various approaches for landslide hazard zoning: a critical review in indian perspectives. Spat. Inform. Res. 25(3), 389–398 (2017)
Kirschbaum, D., Bach, R., Adler, Y., Hong, S., Hill, Lerner-Lam, A.: A global landslide catalog for hazard applications: method, results, and limitations. Nat. Hazards 52(3), 561–575 (2010)
Kwan, J.S.H., Chan, S.L., Cheuk, J.C.Y., Koo, R.C.H.: A case study on an open hillside landslide impacting on a flexible rockfall barrier at Jordan Valley, Hong Kong. Landslides 11(6), 1037–1050 (2014)
Lee, S.A.R.O.: Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int. J. Remote Sens. 26(7), 1477–1491 (2005)
Lee, S., and Biswajeet Pradhan: Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4(1), 33–41 (2007)
Li, H., Chen, Y., Deng, S., Chen, M., Fang, T., Tan, H.: Eigenvector spatial filtering-based logistic regression for Landslide Susceptibility Assessment. ISPRS Int. J. Geo-Information. 8(8), 332 (2019)
Li, Y., Zhou, R., Zhao, G., Li, H., Su, D., Ding, H., Yun, Z.Y.L.Y.K., Ma, C.: Tectonic uplift and landslides triggered by the Wenchuan earthquake and constraints on orogenic growth: Aa case study from Hongchun Gully, Longmen Mountains, Sichuan, China. Quatern. Int. 349, 142–152 (2014)
Ma, J., Tang, H., Hu, X., Bobet, A., Zhang, M., Zhu, T., Song, Y., Mutasim, A.M., M, E.E.: Identification of causal factors for the Majiagou landslide using modern data mining methods. Landslides. 14(1), 311–322 (2017)
Mantovani, F., Soeters, R., Van Westen, C.J.: Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology. 15(3–4), 213–225 (1996)
Martha, T.R., Roy, P., Jain, N., Khanna, K., Mrinalni, K., Kumar, K.V., Rao, P.V.N.: Geospatial landslide inventory of India—an insight into occurrence and exposure on a national scale. Landslides. 18(6), 2125–2141 (2021)
Mathew, J., Jha, V.K., Rawat, G.S.: Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India. Int. J. Remote Sens. 28(10), 2257–2275 (2007)
Mohammady, M., Pourghasemi, H.R., Biswajeet Pradhan: Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J. Asian Earth Sci. 61, 221–236 (2012)
Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C., Jaedicke, C.: Global landslide and avalanche hotspots. Landslides. 3(2), 159–173 (2006)
NASA:. “Global Landslide Catalog.“ NASA. (2020). Accessed 27 June 2020https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4#About
Ozdemir, A., Tolga Altural: A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J. Asian Earth Sci. 64, 180–197 (2013)
Palenzuela, J.A., Marsella, M., Nardinocchi, C., Pérez, J.L., Fernández, T., J Chacón, and, Irigaray, C.: Landslide detection and inventory by integrating LiDAR data in a GIS environment. Landslides. 12(6), 1035–1050 (2015)
Pardeshi, S.D., Sumant, E., Autade, Suchitra, S.P.: Landslide hazard assessment: recent trends and techniques. SpringerPlus 2(1), 523 (2013)
Pham, B., Thai, D.T., Bui, I., Prakash, Dholakia, M.: Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS. J. Geomat. 10, 71–79 (2016)
Pham, B., Thai, D.T., Bui, I., Prakash, Dholakia, M.B.: 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 (2017)
Pham, B., Thai, I., Prakash, S.K., Singh, A., Shirzadi, H., Shahabi, Bui, D.T.: Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. Catena 175, 203–218 (2019)
Pham, B., Thai, I., Prakash, J., Dou, S.K., Singh, P.T., Trinh, H.T., Tran: Tu Minh Le, Tran Van Phong, Dang Kim Khoi, and Ataollah Shirzadi. 2019. “A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers.“ Geocarto International:1–25
Popescu, M.E.: A suggested method for reporting landslide causes. Bull. Int. Assoc. Eng. Geol.-Bulletin de l’Assoc. Int. de Géologie de l’Ingénieur 50(1), 71–74 (1994)
Pradhan, B.: Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J. Indian Soc. Remote Sens. 38(2), 301–320 (2010)
Pradhan, B., Lee, S.: 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 (2010)
Pradhan, B., Seeni, M.I., Bahareh Kalantar: Performance evaluation and sensitivity analysis of expert-based, statistical, machine learning, and hybrid models for producing landslide susceptibility maps. In: Laser Scanning Applications in Landslide Assessment, pp. 193–232. Springer (2017)
Regmi, A., Deep, K.C., Devkota, K., Yoshida, B., Pradhan, H.R., Pourghasemi, T., Kumamoto, Aykut Akgun: Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab. J. Geosci. 7(2), 725–742 (2014)
Sarkar, S., Kanungo, D.P., Patra, A.K., Kumar, P.: GIS based spatial data analysis for landslide susceptibility mapping. J. Mt. Sci. 5(1), 52–62 (2008)
Sharma, A., Sur, U., Singh, P., Rai, P.K., Prashant, K.S.: “Probabilistic landslide Hazard Assessment using statistical information value (SIV) and GIS techniques: A case study of Himachal Pradesh, India.“ Techniques for Disaster Risk Management and Mitigation:197–208. (2020)
Singh, P., Sharma, A., Sur, U., et al.: Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India. Environ. Dev. Sustain. 23, 5233–5250 (2021). https://doi.org/10.1007/s10668-020-00811-0
Sur, U., Singh, P., Meena, S.R.: Landslide susceptibility assessment in a lesser himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data, Geomatics. Nat. Hazards Risk. 11(1), 2176–2209 (2020). https://doi.org/10.1080/19475705.2020.1836038
Sur, U., Singh, P., Rai, P.K., et al.: Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India. Environ. Dev. Sustain. 23, 13526–13554 (2021). https://doi.org/10.1007/s10668-021-01226-1
Sur, U., Singh, P., Meena, S.R., Singh, T.N.: Predicting landslides susceptible zones in the lesser Himalayas by ensemble of per pixel and object-based models. Remote Sens. 14, 1953 (2022). https://doi.org/10.3390/rs14081953
Süzen, M., Lütfi: Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey Eng. Geol. 71(3–4), 303–321 (2004)
Thai Pham, B., Shirzadi, A., Shahabi, H., Omidvar, E., Singh, S.K., Sahana, M., Dawood Talebpour, Asl, Bin Ahmad, B., Kim Quoc, N., Lee, S.: Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability 11(16), 4386 (2019)
Varnes, D.J.: International association of engineering geology commission on landslides and other mass movements on slopes: Landslide hazard zonation: a review of principles and practice. Nat. Hazards, Series. 3. (1984)
Wang, Q., Li, W.: A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys. Geogr. 38(4), 318–337 (2017)
Wang, Q., Li, W., Wu, Y., Pei, Y., Xing, M., Yang, D.: A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models. J. Earth Syst. Sci. 125(3), 645–662 (2016)
Wang, Q., Guo, Y., Li, W., He, J., Wu, Z.: Predictive modeling of landslide hazards in Wen County, Northwestern China based on information value, weights-of-evidence, and certainty factor. Geomatics Nat. Hazards Risk 10(1), 820–835 (2019)
Wilson, J.B.: The twelve theories of co-existence in plant communities: the doubtful, the important and the unexplored. J. Veg. Sci. 22(1), 184–195 (2011)
Yilmaz, I.: 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 (2009)
Yin, K.L., and Yan, T.Z.: Statistical prediction models for instability of metamorphosed rocks. International symposium on landslides. 5. (1988)
Zhang, J., He, P., Jie Xiao, and, Xu, F.: Risk assessment model of expansive soil slope stability based on Fuzzy-AHP method and its engineering application. Geomatics Nat. Hazards Risk. 9(1), 389–402 (2018)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
I am hereby confirming as the corresponding Author of the paper that the no conflict of Interest for publication of this paper. The proposed paper is original and carried out by the authors.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Singh, P., Sur, U., Rai, P.K. et al. Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India). Proc.Indian Natl. Sci. Acad. 89, 600–612 (2023). https://doi.org/10.1007/s43538-023-00171-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s43538-023-00171-z