Advertisement

Landslides

, Volume 14, Issue 1, pp 1–17 | Cite as

A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

  • Dieu Tien BuiEmail author
  • Quoc Phi Nguyen
  • Nhat-Duc Hoang
  • Harald Klempe
Original Paper

Abstract

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC = 0.944) and validation dataset (AUC = 0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.

Keywords

Landslide Fuzzy k-nearest neighbor Differential evolution GIS Quy Hop Vietnam 

Notes

Acknowledgments

This research was supported by the project B2014-02-21 (Hanoi University of Mining and Geology, Vietnam) and was partially supported by University College of Southeast Norway, Bø i Telemark, Norway.

References

  1. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135CrossRefGoogle Scholar
  2. Ayalew L, Yamagishi H (2005) The application of gis-based logistic regression for landslide susceptibility mapping in the Kakuda-yahiko mountains, central Japan. Geomorphology 65:15–31CrossRefGoogle Scholar
  3. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using gis-based weighted linear combination, the case in Tsugawa area of Agano river, Niigata prefecture, Japan. Landslides 1:73–81CrossRefGoogle Scholar
  4. Beven K, Kirkby M, Schofield N, Tagg A (1984) Testing a physically-based flood forecasting model (TOPMODEL) for three UK catchments. J Hydrol 69:119–143CrossRefGoogle Scholar
  5. Chen C-Y, Chang J-M (2015) Landslide dam formation susceptibility analysis based on geomorphic features. Landslides 1-15Google Scholar
  6. Chen H-L, Yang B, Wang G, Liu J, Xu X, Wang S-J, Liu D-Y (2011) A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method. Knowl-Based Syst 24:1348–1359CrossRefGoogle Scholar
  7. Cheng M-Y, Hoang N-D (2013) Groutability estimation of grouting processes with microfine cements using an evolutionary instance-based learning approach. J Comput Civ EngGoogle Scholar
  8. Chung C-J, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452CrossRefGoogle Scholar
  9. Chung CJF, Fabbri AG and Van westen CJ (1995) Multivariate regression analysis for landslide hazard zonation. In: Carrara A and Guzzetti F (eds) Geographical information systems in assessing natural hazards, p 107-133Google Scholar
  10. Conoscenti C, Di Maggio C, Rotigliano E (2008) GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94:325–339CrossRefGoogle Scholar
  11. Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci 12:327–340CrossRefGoogle Scholar
  12. Costanzo D, Chacón J, Conoscenti C, Irigaray C, Rotigliano E (2014) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (Southern Sicily, Italy). Landslides 11:639–653CrossRefGoogle Scholar
  13. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27CrossRefGoogle Scholar
  14. Dai FC, Lee CF (2002) Landslide characteristics, and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228CrossRefGoogle Scholar
  15. Dou J, Tien Bui D, Yunus AP, Jia K, Song X, Revhaug I, Xia H, Zhu Z, Dou J, Tien Bui D, Yunus AP, 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:e0133262CrossRefGoogle Scholar
  16. Duin R, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax D, Verzakov S (2004) Prtools, a matlab toolbox for pattern recognition http://www.prtools.org
  17. Eberhart RC and Shi Y (2001) Particle swarm optimization: developments, applications and resources. Evolutionary Computation, 2001 Proceedings of the 2001 Congress on, IEEE, p 81-86Google Scholar
  18. Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730CrossRefGoogle Scholar
  19. Feizizadeh B, Blaschke T, Nazmfar H (2014) GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digital Earth 7:688–708CrossRefGoogle Scholar
  20. Feuillet T, Coquin J, Mercier D, Cossart E, Decaulne A, Jónsson HP, Sæmundsson þ (2014) Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland: Do paraglacial factors vary over space? Prog Phys Geogr 38:354–377CrossRefGoogle Scholar
  21. Florinsky IV, Eilers RG, Manning G, Fuller L (2002) Prediction of soil properties by digital terrain modelling. Environ Model Softw 17:295–311CrossRefGoogle Scholar
  22. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99CrossRefGoogle Scholar
  23. Gorsevski PV, Jankowski P, Gessler PE (2005) Spatial prediction of landslide hazard using fuzzy k-means and Dempster-Shafer theory. Trans GIS 9:455–474CrossRefGoogle Scholar
  24. Ho VT (2012) Assessment of environmental status in mining areas of the Nghe An and Ha Tinh provinces. North Central Geological Division of Vietnam, HanoiGoogle Scholar
  25. 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. doi: 10.1061/(ASCE)CP.1943-5487.0000557 Google Scholar
  26. Hong H, Pradhan B, Xu C, Tien Bui D (2015a) 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. Hong H, Xu C, Revhaug I, Tien Bui D (2015b) Spatial prediction of landslide hazard at the Yihuang area (China): a comparative study on the predictive ability of backpropagation multi-layer perceptron neural networks and radial basic function neural networks. In: Robbi Sluter C, Madureira Cruz CB, Leal de Menezes PM (eds) Cartography—maps connecting the world. Springer, Champ, pp 175–188CrossRefGoogle Scholar
  28. Hong H, Pradhan B, Jebur M, Bui D, Xu C, Akgun A (2016a) Spatial prediction of landslide hazard at the luxi area (china) using support vector machines. Environ Earth Sci 75:1–14. doi: 10.1007/s12665-015-4866-9
  29. Hong H, Chen W, Xu C, Youssef AM, Pradhan B, Bui D (2016b) Rainfall-induced landslide susceptibility assessment at the chongren area (china) using frequency ratio, certainty factor, and index of entropy. Geocarto International. doi: 10.1080/10106049.2015.1130086
  30. Huang J, Wu P, Zhao X (2013) Effects of rainfall intensity, underlying surface and slope gradient on soil infiltration under simulated rainfall experiments. Catena 104:93–102CrossRefGoogle Scholar
  31. Ingber L (1993) Simulated annealing: practice versus theory. Math Comput Model 18:29–57CrossRefGoogle Scholar
  32. Kavzoglu T, Sahin E, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439CrossRefGoogle Scholar
  33. Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from aster images and an artificial neural network (ANN). Geomorphology 113:97–109CrossRefGoogle Scholar
  34. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern Syst Hum 580-585Google Scholar
  35. Kritikos T, Davies T (2014) 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 12:1051–1075CrossRefGoogle Scholar
  36. Lanni C, Borga M, Rigon R, Tarolli P (2012) Modelling shallow landslide susceptibility by means of a subsurface flow path connectivity index and estimates of soil depth spatial distribution. Hydrol Earth Syst Sci 16:3959–3971CrossRefGoogle Scholar
  37. Lee S, Dan NT (2005) Probabilistic landslide susceptibility mapping on the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ Geol 48:778–787CrossRefGoogle Scholar
  38. Lee JH, Park HJ (2015) Assessment of shallow landslide susceptibility using the transient infiltration flow model and gis-based probabilistic approach. Landslides 1-19Google Scholar
  39. Li S-T, Ho H-F (2009) Predicting financial activity with evolutionary fuzzy case-based reasoning. Expert Syst Appl 36:411–422CrossRefGoogle Scholar
  40. Liu Z-g, Pan Q, Dezert J (2014) Classification of uncertain and imprecise data based on evidence theory. Neurocomputing 133:459–470CrossRefGoogle Scholar
  41. Liu Z-g, Liu Y, Dezert J, Pan Q (2015) Classification of incomplete data based on belief functions and k-nearest neighbors. Knowl-Based Syst 89:113–125CrossRefGoogle Scholar
  42. Maltman A (2012) The geological deformation of sediments. Springer Science & Business MediaGoogle Scholar
  43. Mancini F, Ceppi C, Ritrovato G (2010) GIS and statistical analysis for landslide susceptibility mapping in the Daunia area (Italy). Nat Hazards Earth Syst Sci 10:1851–1864CrossRefGoogle Scholar
  44. Meinhardt M, Fink M, Tünschel H (2015) 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 234:80–97CrossRefGoogle Scholar
  45. Menking JA, Han J, Gasparini NM, Johnson JPL (2013) The effects of precipitation gradients on river profile evolution on the big island of Hawai’i. Geol Soc Am Bull 125:594–608CrossRefGoogle Scholar
  46. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236CrossRefGoogle Scholar
  47. Moore ID, Grayson R, Ladson A (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30CrossRefGoogle Scholar
  48. 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 97:171–191CrossRefGoogle Scholar
  49. Nguyen QH (2014a) Investigation, assessment and warning zonation for landslides in the mountainous regions of Vietnam. Ministry of Natural Resources and Environment of Vietnam, HanoiGoogle Scholar
  50. Nguyen QP (2014b) Assessment of potential environmental impacts from mining activities in central region of Vietnam. Hanoi University of Mining and Geology, HanoiGoogle Scholar
  51. Pariente S (2002) Spatial patterns of soil moisture as affected by shrubs, in different climatic conditions. Environ Monit Assess 73:237–251CrossRefGoogle Scholar
  52. Parpinelli RS, Lopes HS, Freitas A (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6:321–332CrossRefGoogle Scholar
  53. Peduzzi P (2010) Landslides and vegetation cover in the 2005 north Pakistan earthquake: a GIS and statistical quantitative approach. Nat Hazards Earth Syst Sci 10:623–640CrossRefGoogle Scholar
  54. Pham B, Tien Bui D, Pourghasemi H, Indra P, Dholakia MB (2015) Landslide susceptibility assesssment in the uttarakhand area (India) using gis: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 1–19Google Scholar
  55. Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016a) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using gis. Nat Hazards 1–31. doi: 10.1007/s11069-016-2304-2
  56. Pham BT, Bui DT, Dholakia M, Prakash I, Pham HV (2016b) A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotech Geol Eng doi: 10.1007/s10706-016-9990-0
  57. Ponsich A, Coello CC (2011) Differential evolution performances for the solution of mixed-integer constrained process engineering problems. Appl Soft Comput 11:399–409CrossRefGoogle Scholar
  58. Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 122:349–369CrossRefGoogle Scholar
  59. Pradhan B (2011) Manifestation of an advanced fuzzy logic model coupled with geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environ Ecol Stat 18:471–493CrossRefGoogle Scholar
  60. Pradhan B, Lee S (2010) 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 25:747–759CrossRefGoogle Scholar
  61. 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 48:4164–4177CrossRefGoogle Scholar
  62. Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer Science & Business MediaGoogle Scholar
  63. Prosser IP, Rustomji P (2000) Sediment transport capacity relations for overland flow. Prog Phys Geogr 24:179–193CrossRefGoogle Scholar
  64. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. Evol Comput IEEE Trans 13:398–417CrossRefGoogle Scholar
  65. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San MateoGoogle Scholar
  66. Rabonza ML, Felix RP, Lagmay AMFA, Eco RNC, Ortiz IJG, Aquino DT (2015) Shallow landslide susceptibility mapping using high-resolution topography for areas devastated by super typhoon haiyan. Landslides 1-10Google Scholar
  67. Ray RL, Jacobs JM (2007) Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat Hazards 43:211–222CrossRefGoogle Scholar
  68. Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain J Sci 5:1–4Google Scholar
  69. Siegel FR (2015) Countering 21st century social-environmental threats to growing global populations. Springer, BerlinCrossRefGoogle Scholar
  70. Sim J, Kim S-Y, Lee J (2005) Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21:2844–2849CrossRefGoogle Scholar
  71. Steinacher R, Medicus G, Fellin W, Zangerl C (2009) The influence of deforestation on slope (in-) stability. Aust J Earth Sci (Mitteilungen der Österreichischen Geologischen Gesellschaft) 102:90–99Google Scholar
  72. Tan YT, Rosdi BA (2015) FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy k-nearest neighbors with Lempel–Ziv complexity based distance measure. Neurocomputing 148:409–419CrossRefGoogle Scholar
  73. Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and naïve Bayes models. Math Probl Eng 2012:1–26CrossRefGoogle Scholar
  74. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Application of support vector machines in landslide susceptibility assessment for the hoa binh province (Vietnam) with kernel functions analysis. iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society, p 382-389Google Scholar
  75. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171–172:12–29CrossRefGoogle Scholar
  76. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012d) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211CrossRefGoogle Scholar
  77. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012e) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40CrossRefGoogle Scholar
  78. Tien Bui D, Ho TC, Revhaug I, Pradhan B, Nguyen D (2013a) Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based j48 decision tree classifier and its ensembles. In: Buchroithner M, Prechtel N, Burghardt D (eds) Cartography from pole to pole. Springer, Berlin Heidelberg, pp 303–317Google Scholar
  79. Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick O (2013b) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam. Nat Hazards 66:707–730CrossRefGoogle Scholar
  80. Tien Bui D, Pradhan B, Revhaug I, Trung Tran C (2014) A comparative assessment between the application of fuzzy unordered rules induction algorithm and J48 decision tree models in spatial prediction of shallow landslides at Lang Son city, Vietnam. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote sensing applications in environmental research. Springer, Champ, pp 87–111CrossRefGoogle Scholar
  81. Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2015) 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. doi: 10.1007/s10346-015-0557-6 Google Scholar
  82. Tien Bui D, Pham TB, Nguyen Q-P, Hoang N-D (2016) Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of least squares support vector machines and differential evolution optimization: a case study in Central Vietnam. Int J Digital Earth. doi: 10.1080/17538947.2016.1169561
  83. Tsangaratos P, Ilia I (2015) Landslide susceptibility mapping using a modified decision tree classifier in the xanthi perfection, Greece. Landslides 1-16Google Scholar
  84. Wen S, Yeh Y-L, Tang C-C, Phong LH, Van Toan D, Chang W-Y, Chen C-H (2015) The tectonic structure of the song ma fault zone, Vietnam. J Asian Earth Sci 107:26–34CrossRefGoogle Scholar
  85. 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 101:572–582CrossRefGoogle Scholar
  86. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015) 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 1-18Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Dieu Tien Bui
    • 1
    • 2
    Email author
  • Quoc Phi Nguyen
    • 3
  • Nhat-Duc Hoang
    • 4
  • Harald Klempe
    • 1
  1. 1.Geographic Information System Group, Department of Business Administration and Computer ScienceUniversity College of Southeast NorwayBø i TelemarkNorway
  2. 2.Faculty of Geomatics and Land AdministrationHanoi University of Mining and GeologyHanoiVietnam
  3. 3.Department of Environmental SciencesHanoi University of Mining and GeologyHanoiVietnam
  4. 4.Faculty of Civil Engineering, Institute of Research and DevelopmentDuy Tan UniversityDanangVietnam

Personalised recommendations