1 Institute of Marine Geology and Geophysics, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi, Vietnam;;;

2 Graduate University of Science and Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi, Vietnam;

3 Center for Agricultural Meteorological Research, Vietnam Institute of Meteorology, Hydrology and Climate change, No.23 - 62 Alley, Nguyen Chi Thanh Road, Dong Da District, Hanoi Vietnam;

*Corresponding author:; Tel.: +84–985150307


This study shows the results of landslide susceptibility mapping for the southwest region of Quang Nam province using the Weights of Evidence (WoE) model. The input data consists of a landslide inventory and ten influencing factors, i.e., geology, distance to fault, elevation, relief amplitude, slope, aspect, rainfall, soil type, land use, and distance to road. The landslide inventory was constructed from three principal sources:  fieldwork survey, legacy data from previous studies, and additional analytical data from high-resolution Google Earth satellite imagery. The landslide locations were randomly categorized into two parts in the ratio 70/30: 70% (811 landslides) for modeling and 30% (348 landslides) for verification. All input data are normalized and constructed into the GIS landslide database. The results of the multicollinearity test show that no collinearity existed between ten input variables. The computation of the weights for classes of influencing factors from 70% of the landslide data using the WoE model has allowed the establishment of the landslide susceptibility map. The model performance was evaluated by using the receiver operating characteristic (ROC) analysis. The area under the curve (AUC) was computed for the success rate curve (using 70% landslide data) and the prediction rate curve (using 30% landslide data) at 0.855 and 0.844, respectively. Thus, it can be confirmed that the landslide susceptibility mapping based on the WoE model was very reliable in the study area.


Cite this paper

Tuan, T.A.; Tam, T.T.; Hong, P.V.; Nguyet, N.T.A. Landslide susceptibility mapping based on the Weights of Evidence model for mountainous areas of Quang Nam province. Vietnam. J. Hydro-Meteorol. 2023, 17, 31-45.


1. World Bank Group. Climate-resilient development in Vietnam: strategic directions for the World Bank. Washington, D.C, 2011. Available online: (accessed on 12 May 2023).
2. Ministry of Natural Resources and Environment of The Socialist Republic of Vietnam. National Disaster Risk in Viet Nam in the Period 2006-2016 and Forecasting and Warning System. 11th Emergency Preparedness Working Group Meeting Nha Trang, Viet Nam, 2017. Available online: (accessed on 12 May 2023).
3. Meinhardt, M.; Fink, M.; Tünschel, H. Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 2015, 234, 80-97.

4. Tien Bui, D.; Tuan, T.A.; Klempe, H.; Pradhan, B.; Revhaug, I. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 2016, 13, 361–378.

5. Tien Bui, D.; Tuan, T.A.; Hoang, N.D.; Thanh, N.Q.; Nguyen, D.B.; Van Liem, N.; Pradhan, B. 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 2017,  14, 447–458.

6. Tuan, T.A.; Dan, N.T. Research the landslide susceptibility and zoning in the Son La hydroelectricity area by the Saaty's Analytical Hiearchy Process (AHP). J. Sci. Earth 2012, 3, 223–232.

7. Tuan, T.A.; Pha, P.D.; Tam, T.T.; Dui, D.T. A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas. IEEE Access. 2023, 11, 69495-69511.

8. Provincial People's Committee of Quang Nam. The report of plans responds to natural hazards according to risk levels in the context of the COVID-19 epidemic in Quang Nam province (in Vietnamese), 2021. Available online: (accessed on 22 May 2023).

9. Merghadi, A.; Yunus, A.P.; Dou, J.; Whiteley, J.; Thai Pham, B.; Bui, D.T.; Avtar, R.; Abderrahmane, B. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Rev. 2020, 207, 103225.

10. Shano, L.; Raghuvanshi, T.K.; Meten, M. Landslide susceptibility evaluation and hazard zonation techniques–a review. Geoenviron. Disasters 2020, 7(1), 1-19.

11. Yong, C.; Jinlong, D.; Fei, G.; Bin, T.; Tao, Z.; Hao, F.; Li, W.; Qinghua, Z. Review of landslide susceptibility assessment based on knowledge mapping. Stochastic Environ. Res. Risk Assess. 2022, 36, 2399–2417.

12. Saro, L.; Min, K. Statistical analysis of landslide susceptibility at Yongin, Korea. Environ. Geology 2001, 40(9), 1095–1113.

13. Mind’je, R.; Li, L.; Nsengiyumva, J.B.; Mupenzi, C.; Nyesheja, E.M.; Kayumba, P. M.; Gasirabo, A.; Hakorimana, E. Landslide susceptibility and influencing factors analysis in Rwanda. Environ. Dev. Sustainability 2020, 22(8), 7985–8012.

14. Dahal, R.K.; Hasegawa, S.; Nonomura, A.; Yamanaka, M.; Dhakal, S.; Paudyal, P. Predictive modeling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of evidence. Geomorphology 2008, 102(3–4), 496–510.

15. Pradhan, B.; Oh, H.J.; Buchroithner, M. Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat. Nat. Hazards Risk 2010, 1, 199–223.

16. Thanh, D.C.; Binh, P.T.; Dam, N.D. Using weights of evidence (WOE) for landslide susceptibility mapping in Quang Nam province. J. Sci. Technol.  Civil Eng. 202216(2V), 139–152.

17. Bui, D.T.; Lofman, O.; Revhaug, I.; Dick, O. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat. Hazards 2011, 59, 1413–1444.

18. Schicker, R.; Moon, V. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 2012, 161, 40–57.

19. Ayalew, L.; Yamagishi, H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 2005, 65(1-2), 15–31.

20. Baeza, C.; Corominas, J. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group 2001, 26(12), 1251–1263.  

21. Pham, B.T.; Prakash, I. Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping. Geocarto Int. 2019, 34(3), 316–333.

22. Duc, D.M.; Lieu, T.M.; Binh, T.Q.; Hang, V.T.; Van, H.P.; Vinh, H.D.; Tan, T.D.; Anh, G.Q.; Ngoc, D.M.; Duc, D.M. Landslide hazard prediction along the mountainous transport arteries in Quang Nam province and the adaptation measures (Vietnamese). Hanoi University of Science, Vietnam National University, Hanoi, Hanoi, Rep. ĐTĐL.CN-23/17, 2020.

23. Hung, L.Q.; Van, N.T.H.; Van, S.P.; Ninh, N.H.; Tam, N.; Huyen, N.T. Landslide inventory mapping in the fourteen Northern provinces of Vietnam: Achievements and difficulties. In Advancing Culture of Living With Landslides, Sassa, K.; Mikoš, M.; Yin, Y.  Eds.; Springer: Cham, Switzerland, 2017, pp. 501–510.

24. Tan, M.T.; Van, H.V.; Tan, N.T.; Vinh, H.Q.; Van, L.N.; Luong, L.D.; Ha, T.T.; Van, T.N.; Thuy, H. L.Th.; Anh, L.T.; Van, T.T.T. Landslide hazard assessment by geological and geomorphological methods integrated with the GIS optimal weighting model in river basins in Thua Thien Hue, Quang Nam, and Da Nang areas, proposing solutions prevent (Vietnamese). Inst. Geol. Sci. Vietnam Acad. Sci. Technol. Hanoi, Vietnam, Rep. VAST 09.01/11-12, 2014, 2014.

25. Thanh, N. Q.; Yem, N. T.; Anh, T. T.; Phuong, N. T.; Cau, N. T.; Ngu, N. D.; Hieu, N. T.; Dai, H. Van; Thái, T. H.; Cong, N. T.; Minh, N. Le; Hoang, N. Van; Lien, V. T. H.; Tien, N. V.; Tuan, T. A.; Tai, N. T.; Kien, N. T.; Hung, N. Van; Thom, B. Van; Hau, D. T. To study, supplement and develop a map of natural disasters in Vietnam’s mainland based on research results from 2000 up to now (Vietnamese). Inst. Geological Sci., Vietnam Acad. Sci. Technol., Hanoi, Vietnam, Rep. KC.08.28/11-15, 2015.

26. Bonham-Carter, G.F. Agterberg, F.P.; Wright, D.F. Weights of evidence modeling: a new approach to mapping mineral potential. In Statistical Applications in the Earth Sciences, Agterberg, F.P., Bonham-Carter G.F., Eds.; Geol. Survey Canada Paper, 1989, 89-9, pp. 171–183.

27. Kayastha, P.; Dhital. MR.; De Smedt, F. Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat. Hazards 2012, 63, 479–498.

28. Neuhäuser, B.; Terhorst, B. Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic Escarpment (SW-Germany). Geomorphology 2007, 86, 12–24.

29. Polykretis, C.; Chalkias, C. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models. Nat. Hazards 2018, 93, 249–274.

30. van Westen, C.J.; Rengers, N.; Soeters, R. Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment. Nat. Hazards 200330, 399–419.

31. Bonham-Carter, G.F. Geographic information systems for geoscientists, modeling with GIS, Pergamon, Press, Ontario, 1994, pp. 398.

32. Lee, S.; Choi, J.; Min, K. Landslide susceptibility analysis and verification using the Bayesian probability model. Env. Geol2002, 43, 120–131.

33. Menard, S. Applied Logistic Regression Analysis (Sage University Paper Series on Quantitative Applications in the Social Sciences), series no. 106, 2nd ed.; ThousandOaks, CA: Sage, 1995.

34. Brenning, A. Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat. Hazards Earth Syst. Sci. 2005, 5(6), 853–862.

35. Jenks, G.F.The data model concept in statistical mapping. Int. Yearb Carto 1967, 7, 186–190.