Skip to main content

GIS-based landslide susceptibility mapping method and Shannon entropy model: a case study on Sakaleshapur Taluk, Western Ghats, Karnataka, India


Landslides are one of the most prevalent natural hazards, and they have significant socioeconomic consequences all around the world. Heavy rainfall is one of the main causes of landslides and thus extremely typical phenomena in the steep terrain of the Western Ghats area. Landslides are less common in the Western Ghats region of Karnataka province than they have been in the Himalayan regions. The study aims to categorize landslide-prone regions using the Shannon entropy (SE) approach in combination with remote sensing and GIS techniques. All thematic layers were measured using the SE model and then weighted into three categories: low, moderate, and high landslide-susceptible zones (LSZ). According to the Shannon entropy value, the most important variable for a landslide is the distance from drainage, followed by drainage density, slope, rainfall, and soil in the research region. The landslide point and landslide density of the study region were used to validate the study. Residents and the study region’s authorities may find the final landslide susceptibility maps informative.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Akinci H, Yavuz Ozalp A (2021) Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophy 69:725–745.

    Article  Google Scholar 

  2. Akgun Aykut, Dag Serhat, Bulut Fikri (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology 54:1127–1143

  3. Alimohammadlou Y, Tanyu BF, Abbaspour A (2021) Automated landslide detection model to delineate the extent of existing landslides. Nat Hazards 107:1639–1656.

    Article  Google Scholar 

  4. Atkinson P, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geophys J Roy Astron Soc 130:55–64

  5. Banshutu Rajeshwar, Versain D Laxmi, Pandey D Desh (2020) Comparative Evaluation of GIS Based Landslide Hazard Zonation Maps Using Different Approaches. Arab J Geosci 93(6):684–692

  6. Brabb EE (1984) Innovative approaches to landslide hazard mapping. In: Proceedings 4th International Symposium on Landslides, Toronto 1: 307–324

  7. Bui T Dieu, Pradhan Biswajeet, Lofman Owe, Revhaug Inge (2012) Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models. Mathematical Problems in Engineering 26

  8. Carrara A, Guzzetti F (eds) (n.d.) Geographical Information Systems in Assessing Natural hazards. The Netherlands: Kluwer Academic Publishers, Dordrecht, pp 135–175

  9. Chen Tao, Zhu Li, Niu Rui-quing, Trinder C John (2020) Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. Journal of Mountain Science 17:670–685

  10. Chen W, Pourghasemi HR, Panahi M (2017) Spatial prediction of landslide susceptibility using an adaptive neurofuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85

  11. Clerici Aldo, Perego Susanna, Tellini, Claudio Paolo, Vescovi (2006) A GIS-based automated procedure for landslide susceptibility mapping by the Conditional Analysis method: The Baganza valley case study (Italian Northern Apennines). Environmental Geology 50:941–961

  12. Eeckhaut VDN, Vanwalleghem, Poesen, Govers Gerard (2006) Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology 76(3):392–410

  13. Farooq S, Akram MS (2021) Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas. Arab J Geosci 14:824.

    Article  Google Scholar 

  14. Fayez L, Thai Pham B, Solanki HA, Pazhman D, Dholakia MB, Khalid M, Prakash I (2018) 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

    Google Scholar 

  15. Gokceoglu Candan, Sonmez H, Ercanoglu Murat (2000) Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey. Engineering Geology 55:277–296

  16. Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999)Land- slide hazard evaluation: an aid to a sustainable development. Geomorphology 31:181–216

  17. Guzzetti F, Galli M, Reichenbach P, Ardizzone F, Cardinali M (2006) Landslide hazard assessment in the Collazzone area, Umbria, central Italy. Natural Hazards and Earth System Sciences 6:115–131

  18. Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016)GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9:112

    Article  Google Scholar 

  19. Hongliang Z, Leihua Y, Gang M (2017) A fuzzy comprehensive evaluation method based on AHP and entropy for a landslide susceptibility map. Entropy 19:396

    Article  Google Scholar 

  20. Huang F, Chen J, Du Z (2020) Landslide susceptibility prediction considering regional soil erosion based on machine-learning models. ISPRS Int J Geo-Inform 9:377.

    Article  Google Scholar 

  21. Hung LQ, Van NTH, Duc DM, Le TCH, Son PV, Khanh NH, Binh LT (2015) Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam). Landslides 13:1285–1301 

  22. Kanungo D, Arora M, Sarkar S, Gupta R (2009) Landslide susceptibility zonation (LSZ)mapping- a review. J South Asia Disaster Stud 2:81–105

    Google Scholar 

  23. Kaur H, Gupta S, Parkash S, Thapa R, Gupta A, Khanal GC (2019) Evaluation of landslide susceptibility in a hill city of Sikkim Himalaya with the perspective of hybrid modelling techniques. Ann GIS 25:113–132.

    Article  Google Scholar 

  24. Khosravi Khabat, Pourghasemi R Hamid, Chapi Kamran, Bahri Masoumeh (2016) Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental Monitoring and Asessment 188:656

  25. Lee D-H, Kim Y-T, Lee S-R(2020) Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sens 12:1194.

    Article  Google Scholar 

  26. Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology 50:847–855

  27. LI XJ, Chen YN, Ouyang H (2002) Analysis on sand disaster with disaster entropy method. Arid Land Geography 25(4):350–353

  28. Mahdadi F, Adberrahmane B, Riheb H, Kanugo DP (2018)GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria. Arab J Geosci 11(17)

  29. Mon DL, Cheng CH, Lin JC (1994) Evaluation weapon system using fuzzy analytic hierarchy process based on entropy weight. Fuzzy Sets and Systems 62(2):127–134

  30. Naveen J, Sitharam TG (2014) Assessment of seismically induced landslide hazard for the state of Karnataka using GIS technique. J Indian Soc Remote Sens (March 2014) 42(1):73–89.

    Article  Google Scholar 

  31. Pham BT, Prakash I, Jaafari A, Bui DT (2018a) Spatial prediction of rainfall-induced landslides using aggregating one-dependence estimators’ classifier. J Indian Soc Remote Sens 2018(46):1457–1470

    Article  Google Scholar 

  32. Pham BT, Prakash I, Khosravi K, Chapi K, Trinh PT, Nog TQ, Hossini SV, Bui DT (2018b) A comparison of support vector machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto Int 34:1385–1407

    Article  Google Scholar 

  33. Pham T Binh, Bui T Dieu, Pourghasemi R Hamid, Indra Prakash, Dholakia M B (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149(1):52–63

  34. Pham, B. T, Tien, Bui D, Prakash I, Dholakia M (2016) Evaluation of Predictive Ability of Support Vector Machines and Naïve Bayes Methods for Spatial Prediction of Landslides in Uttarkhand State (India) Using GIS. J. Geomatic 10(1):71–79

  35. 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

    Article  Google Scholar 

  36. Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research 45:1244–1256

  37. Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

  38. Pradhan Biswajeet, Singh R, Buchroithner Manfred (2006) Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Advances in Space Research 37:698–709

  39. Pradhan, B, Lee, S Buchroithner MF (2010b) A GIS-based back-propagation neural network model and its cross application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems 34:216–235

  40. Pourghasemi HR, Jirandeh G Abbas, Pradhan Biswajeet, Xu Chong (2012) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran 2. Journal of Earth System Science 122(2)

  41. Ramakrhisnan M, Vaidyanathan R (2008) Geology of India. Geol Soc India, Bangaluru 1:232–233

    Google Scholar 

  42. Regmi D Amar, Devkota C Krishna, Yoshida K, Pradhan Biswajeet, Pourghasemi R Hamid, Kumato Takashi,  Akgun Aykut (2013) Application of Frequency ratio, statistical index and weight of evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725-742

  43. Saha Anik, Mandal Sujit, Saha Sunil (2020)Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods. SN Applied Sciences 1647

  44. Shannon CE (1948) A Mathematical Theory of Communication. Bell Telephone System Technical Publications 27:379–423

  45. Sitharam TG, James N, Vipin KS, Ganesha Raj K (2012) A study on seismicity and seismic hazard for Karnataka State. J Earth Syst Sci 121(2):475–490

    Article  Google Scholar 

  46. Tan MT, Tao NV (2014) Studying landslides in Thua Thien Hue province. Vietnam J Earth Sci 36(2):121–130.

  47. Van Thom B, Son PQ, Van Hung P, V. A. N. (2016) Research assessment landslide and sedimentation of Hoa Binh hydropower reservoir. Earth Sci 38(1):131–142

  48. Varnes DJ (1978) Slope movement types and processes, in Schuster, R L, Krizek, R J eds Landslides—Analysis and control. National Research Council, Washington, D C, Transportation Research Board, Special Report 176:11–33

  49. Wan SA (2009) A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Eng Geol 108:237–251

  50. Yi CX, Shi PJ (1994) Entropy production and natural hazard. J Beijing Norm Univ 30(2):276–280

  51. Yilmaz I (2010) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks Environ. Earth Sci 60(3):505–519

  52. Youssef M Ahmed, Pourghasemi R Hamid (2010) Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geoscience Frontiers 12(2):639–655

  53. Youssef M Ahmed, Pourghasemi R Hamid, El Hadded A Bosy, Dhahry K Basem (2015) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia. Bulletin of Engineering Geology and the Environment

  54. Zhao Y, Wang R, Jiang Y, Liu H, Wei Z (2019)GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing. Southeast China Eng Geol 259:105147.

    Article  Google Scholar 

Download references


The first author would like to thanks ICSSR (Indian Council for Social Science Research, Ministry of Home Affairs, Government of India) for financial support, and thanks to the Center for Natural Hazards and Disaster Studies, University of Madras, Chennai, Tamil Nadu. The authors would like to thank the editors and the reviewers for their valuable contributions.

Author information



Corresponding authors

Correspondence to Aneesah Rahaman or Ramamoorthy Ayyamperumal.

Ethics declarations

Ethical statement

We declare that this manuscript has not been previously published and is not under consideration for publication elsewhere, and that its publication is approved by all authors. All authors have contributed sufficiently to the paper to be included as authors.

Conflict of interest

The authors declare that they have no competing interests.

Additional information

This article is part of the Topical Collection on Recent advanced techniques in water resources management

Responsible Editor: Venkatramanan Senapathi

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rahaman, A., Venkatesan, M.S. & Ayyamperumal, R. GIS-based landslide susceptibility mapping method and Shannon entropy model: a case study on Sakaleshapur Taluk, Western Ghats, Karnataka, India. Arab J Geosci 14, 2154 (2021).

Download citation


  • Landslide
  • Shannon entropy
  • GIS
  • Susceptibility
  • Western Ghats