Natural Hazards

, Volume 92, Issue 3, pp 1871–1887 | Cite as

Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: a case study in Vietnam

  • Nhat-Duc Hoang
  • Dieu Tien Bui
Original Paper


Shallow landslide represents one of the most devastating morphodynamic processes that bring about great destructions to human life and infrastructure. Landslide spatial prediction can significantly help government agencies in land use and mitigation measure planning. Nevertheless, landslide spatial modeling remains a very challenging problem due to its inherent complexity. This study proposes an integration of geographical information system (GIS) and gene expression programming (GEP) for predicting rainfall-induced shallow landslide occurrences in Son La Province, Vietnam. A landslide inventory map has been constructed based on historical landslide locations. Furthermore, a dataset which features 12 influencing factors is collected using GIS technology. Based on the GEP algorithm and the collected dataset, an empirical model for spatial prediction of the shallow landslide has been established by means of natural selection. The predictive capability of the model has been verified by the area under the curve calculation. Experimental results point out that the newly proposed approach is a promising tool for shallow landslide prediction.


Shallow landslide Rainfall-induced Gene expression programming Geographical information system Artificial intelligence 


  1. Ahlheim M, Fror O, Heinke A, Keil A, Duc NM, Dinh PV, Saint-Macary C, Zeller M (2009) Landslides in mountainous regions of northern Vietnam: causes, protection strategies and the assessment of economic losses. Int J Ecol Econ Stat 15(F09):108–130Google Scholar
  2. Alkroosh I, Nikraz H (2011) Correlation of pile axial capacity and CPT data using gene expression programming. Geotech Geol Eng 29(5):725–748CrossRefGoogle Scholar
  3. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135CrossRefGoogle Scholar
  4. Beale MH, Hagan MT, Demuth HB (2012) Neural network toolbox user’s guide. The MathWorks, IncGoogle Scholar
  5. Borrelli L, Cofone G, Coscarelli R, Gullà G (2014) Shallow landslides triggered by consecutive rainfall events at Catanzaro strait (Calabria–Southern Italy). J Maps 11(5):730–744CrossRefGoogle Scholar
  6. Browne NPA, dos Santos MV (2010) Adaptive representations for improving evolvability, parameter control, and parallelization of gene expression programming. Appl Comput Intell Soft Comput 2010:19CrossRefGoogle Scholar
  7. Chang K-T, Chiang S-H, Chen Y-C, Mondini AC (2014) Modeling the spatial occurrence of shallow landslides triggered by typhoons. Geomorphology 208:137–148CrossRefGoogle Scholar
  8. Chauhan S, Sharma M, Arora MK, Gupta NK (2010) Landslide susceptibility zonation through ratings derived from artificial neural network. Int J Appl Earth Obs 12(5):340–350CrossRefGoogle Scholar
  9. Chen S-C, Chang C-C, Chan H-C, Huang L-M, Lin L-L (2013) Modeling typhoon event-induced landslides using GIS-based logistic regression: a case study of Alishan forestry railway, Taiwan. Math Probl Eng 2013:9Google Scholar
  10. 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(3):381–391CrossRefGoogle Scholar
  11. Dan NT, Tuan TA, Thu TH, Hong PV, Hung LQ, Luong NV, Hai NT, Nhung H, Ha NTV, Thu DH, Thanh LV, Hien D, Mai D (2011) Application of remote sensing, GIS, and GPS for the study of landslides at the Son La hydropower basin and proposed remedial measures. In: Technical Report, Institute of Marine Geology & Geophysics, Vietnam Academy of Science and Technology, HanoiGoogle Scholar
  12. Dou J, Tien Bui D, Yunus A, Jia K, Song X, Revhaug I, Xia H, Zhu Z (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS ONE 10(7):0133262CrossRefGoogle Scholar
  13. Ebtehaj I, Bonakdari H, Zaji AH, Azimi H, Sharifi A (2015) Gene expression programming to predict the discharge coefficient in rectangular side weirs. Appl Soft Comput 35:618–628CrossRefGoogle Scholar
  14. Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T, Ghorbani H (2015) Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). J Hydrol 529:1590–1600 (in press) CrossRefGoogle Scholar
  15. Ercanoglu M (2005) Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks. Nat Hazards Earth Syst Sci 5(6):979–992CrossRefGoogle Scholar
  16. Ferreira C (2001) Gene expression programming a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129Google Scholar
  17. Ferreira C (2006) Gene expression programming mathematical modeling by an artificial intelligence. Springer, Berlin, HeidelbergGoogle Scholar
  18. Ferreira C (2013) Getting started with classification. GeneXproTools,
  19. Gandomi AH, Alavi AH, Kazemi S, Gandomi M (2014) Formulation of shear strength of slender RC beams using gene expression programming, part I: without shear reinforcement. Autom Constr 42:112–121CrossRefGoogle Scholar
  20. Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11CrossRefGoogle Scholar
  21. Gomez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Eng Geol 78(1–2):11–27CrossRefGoogle Scholar
  22. Hoang N-D, Pham A-D (2016) Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: a multinational data analysis. Expert Syst Appl 46(15):60–68CrossRefGoogle Scholar
  23. Hoang N-D, Tien Bui D (2018) GIS-based landslide spatial modeling using batch-training back-propagation artificial neural network: a study of model parameters. In: Tien Bui D, Ngoc Do A, Bui H-B, Hoang N-D (eds) Advances and applications in geospatial technology and earth resources: proceedings of the international conference on geo-spatial technologies and earth resources 2017, Springer, Cham, pp 239–254Google Scholar
  24. Hoang N-D, Tien-Bui D (2016) A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. J Comput Civ Eng 30(5):04016001CrossRefGoogle Scholar
  25. Hoang N-D, Chen C-T, Liao K-W (2017) Prediction of chloride diffusion in cement mortar using multi-gene genetic programming and multivariate adaptive regression splines. Measurement 112(Supplement C):141–149CrossRefGoogle Scholar
  26. Hong H, Pradhan B, Xu C, Tien Bui D (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 133:266–281CrossRefGoogle Scholar
  27. Iovine GR, Greco R, Gariano S, Pellegrino A, Terranova O (2014) Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Nat Hazards 73(1):111–136CrossRefGoogle Scholar
  28. Jolliffe IT (2010) Principal component analysis, 2nd edn. Springer, New YorkGoogle Scholar
  29. Kavzoglu T, Sahin E, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439CrossRefGoogle Scholar
  30. Kayadelen C (2011) Soil liquefaction modeling by genetic expression programming and neuro-fuzzy. Expert Syst Appl 38(4):4080–4087CrossRefGoogle Scholar
  31. Khan M, Azamathulla HM, Tufail M, Ab Ghani A (2012) Bridge pier scour prediction by gene expression programming. Water Manag 165(WM9):481–493Google Scholar
  32. Lima P, Steger S, Glade T, Tilch N, Schwarz L, Kociu A (2017) Landslide susceptibility mapping at national scale: a first attempt for Austria. Springer, New York, pp 943–951Google Scholar
  33. Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47(3):411–435CrossRefGoogle Scholar
  34. MathWorks (2012) Statistics Toolbox. The MathWorks, IncGoogle Scholar
  35. Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, Kanevski M (2014) Machine learning feature selection methods for landslide susceptibility mapping. Math Geosci 46(1):33–57CrossRefGoogle Scholar
  36. Mondini AC, Marchesini I, Rossi M, Chang K-T, Pasquariello G, Guzzetti F (2013) Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data. Geomorphology 201:135–147CrossRefGoogle Scholar
  37. Mousavi SM, Aminian P, Gandomi AH, Alavi AH, Bolandi H (2012) A new predictive model for compressive strength of HPC using gene expression programming. Adv Eng Softw 45(1):105–114CrossRefGoogle Scholar
  38. Nazari A, Pacheco Torgal F (2013) Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP. Expert Syst Appl 40(14):5427–5438CrossRefGoogle Scholar
  39. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191CrossRefGoogle Scholar
  40. Nguyen KL (2010). Assessing landslide vulnerability in Vietnam: conceptual framework and proposed research techniques. In: Proceedings of the awareness of the need for environmental protection—a role for higher education, 2010, Ho Chi Minh City, Vietnam, pp 131–139Google Scholar
  41. Nguyen Q-K, Tien Bui D, Hoang N-D, Trinh P, Nguyen V-H, Yilmaz I (2017) A novel hybrid approach based on instance based learning classifier and rotation forest ensemble for spatial prediction of rainfall-induced shallow landslides using GIS. Sustainability 9(5):813CrossRefGoogle Scholar
  42. Park HJ, Lee JH, Woo I (2013) Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach. Eng Geol 161:1–15CrossRefGoogle Scholar
  43. Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177CrossRefGoogle Scholar
  44. Schmaltz EM, Steger S, Glade T (2017) The influence of forest cover on landslide occurrence explored with spatio-temporal information. Geomorphology 290:250–264CrossRefGoogle Scholar
  45. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219CrossRefGoogle Scholar
  46. Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:9899CrossRefGoogle Scholar
  47. Shirzadi A, Bui DT, Pham BT, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76(2):60CrossRefGoogle Scholar
  48. Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199CrossRefGoogle Scholar
  49. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes models. Math Probl Eng 2012:26CrossRefGoogle Scholar
  50. Tien Bui D, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2013) 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). Geomat Nat Hazards Risk 6(3):243–271Google Scholar
  51. Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) 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 13(2):361–378. CrossRefGoogle Scholar
  52. Tsangaratos P, Benardos A (2014) Estimating landslide susceptibility through a artificial neural network classifier. Nat. Hazards 74(3):1489–1516CrossRefGoogle Scholar
  53. Tuan TA, Dan NT (2012) Landslide susceptibility mapping and zoning in the Son La hydropower catchment area using the analytical hierarchy process. J Sci Earth (Vietnamese) 3:223–232Google Scholar
  54. Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114CrossRefGoogle Scholar
  55. Wu Z, Fan H, Liu G (2015) Forecasting construction and demolition waste using gene expression programming. J Comput Civ Eng 29(5):04014059CrossRefGoogle Scholar
  56. Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA 72(1):1–12CrossRefGoogle Scholar
  57. 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(4):572–582CrossRefGoogle Scholar
  58. Yem (2006) Assessment of landslides, flash floods, and debris flows in selected prone areas in the northern mountainous Vietnam and recommendation of remedial measures to prevent and mitigate potential damages. In: National project report, VietnamGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Research and Development, Faculty of Civil EngineeringDuy Tan UniversityDa NangVietnam
  2. 2.Geographic Information System Group, Department of Business and ITUniversity College of Southeast NorwayBø i TelemarkNorway

Personalised recommendations