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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

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.

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References

  • Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of gis-based logistic regression for landslide susceptibility mapping in the Kakuda-yahiko mountains, central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • 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 1:73–81

    Article  Google Scholar 

  • Beven K, Kirkby M, Schofield N, Tagg A (1984) Testing a physically-based flood forecasting model (TOPMODEL) for three UK catchments. J Hydrol 69:119–143

    Article  Google Scholar 

  • Chen C-Y, Chang J-M (2015) Landslide dam formation susceptibility analysis based on geomorphic features. Landslides 1-15

  • 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 24:1348–1359

    Article  Google Scholar 

  • 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 Eng

  • Chung C-J, Fabbri AG (2008) Predicting landslides for risk analysis—spatial models tested by a cross-validation technique. Geomorphology 94:438–452

    Article  Google Scholar 

  • 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 107-133

  • Conoscenti C, Di Maggio C, Rotigliano E (2008) GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy). Geomorphology 94:325–339

    Article  Google Scholar 

  • 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 12:327–340

    Article  Google Scholar 

  • 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 11:639–653

    Article  Google Scholar 

  • Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27

    Article  Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics, and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • 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:e0133262

    Article  Google Scholar 

  • 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

  • Eberhart RC and Shi Y (2001) Particle swarm optimization: developments, applications and resources. Evolutionary Computation, 2001 Proceedings of the 2001 Congress on, IEEE, p 81-86

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Article  Google Scholar 

  • 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 7:688–708

    Article  Google Scholar 

  • 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 38:354–377

    Article  Google Scholar 

  • Florinsky IV, Eilers RG, Manning G, Fuller L (2002) Prediction of soil properties by digital terrain modelling. Environ Model Softw 17:295–311

    Article  Google Scholar 

  • Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99

    Article  Google Scholar 

  • Gorsevski PV, Jankowski P, Gessler PE (2005) Spatial prediction of landslide hazard using fuzzy k-means and Dempster-Shafer theory. Trans GIS 9:455–474

    Article  Google Scholar 

  • Ho VT (2012) Assessment of environmental status in mining areas of the Nghe An and Ha Tinh provinces. North Central Geological Division of Vietnam, Hanoi

    Google Scholar 

  • 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 

  • 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 133:266–281

    Article  Google Scholar 

  • 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 175–188

    Chapter  Google Scholar 

  • 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

  • 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

  • Huang J, Wu P, Zhao X (2013) Effects of rainfall intensity, underlying surface and slope gradient on soil infiltration under simulated rainfall experiments. Catena 104:93–102

    Article  Google Scholar 

  • Ingber L (1993) Simulated annealing: practice versus theory. Math Comput Model 18:29–57

    Article  Google Scholar 

  • Kavzoglu T, Sahin E, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439

    Article  Google Scholar 

  • Kawabata D, Bandibas J (2009) Landslide susceptibility mapping using geological data, a DEM from aster images and an artificial neural network (ANN). Geomorphology 113:97–109

    Article  Google Scholar 

  • Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern Syst Hum 580-585

  • 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 12:1051–1075

    Article  Google Scholar 

  • 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 16:3959–3971

    Article  Google Scholar 

  • 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 48:778–787

    Article  Google Scholar 

  • Lee JH, Park HJ (2015) Assessment of shallow landslide susceptibility using the transient infiltration flow model and gis-based probabilistic approach. Landslides 1-19

  • Li S-T, Ho H-F (2009) Predicting financial activity with evolutionary fuzzy case-based reasoning. Expert Syst Appl 36:411–422

    Article  Google Scholar 

  • Liu Z-g, Pan Q, Dezert J (2014) Classification of uncertain and imprecise data based on evidence theory. Neurocomputing 133:459–470

    Article  Google Scholar 

  • 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 89:113–125

    Article  Google Scholar 

  • Maltman A (2012) The geological deformation of sediments. Springer Science & Business Media

  • 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 10:1851–1864

    Article  Google Scholar 

  • 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 234:80–97

    Article  Google Scholar 

  • 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 125:594–608

    Article  Google Scholar 

  • 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 61:221–236

    Article  Google Scholar 

  • Moore ID, Grayson R, Ladson A (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • 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 97:171–191

    Article  Google Scholar 

  • Nguyen QH (2014a) Investigation, assessment and warning zonation for landslides in the mountainous regions of Vietnam. Ministry of Natural Resources and Environment of Vietnam, Hanoi

    Google Scholar 

  • Nguyen QP (2014b) Assessment of potential environmental impacts from mining activities in central region of Vietnam. Hanoi University of Mining and Geology, Hanoi

    Google Scholar 

  • Pariente S (2002) Spatial patterns of soil moisture as affected by shrubs, in different climatic conditions. Environ Monit Assess 73:237–251

    Article  Google Scholar 

  • Parpinelli RS, Lopes HS, Freitas A (2002) Data mining with an ant colony optimization algorithm. IEEE Trans Evol Comput 6:321–332

    Article  Google Scholar 

  • Peduzzi P (2010) Landslides and vegetation cover in the 2005 north Pakistan earthquake: a GIS and statistical quantitative approach. Nat Hazards Earth Syst Sci 10:623–640

    Article  Google Scholar 

  • 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 1–19

  • 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

  • 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

  • Ponsich A, Coello CC (2011) Differential evolution performances for the solution of mixed-integer constrained process engineering problems. Appl Soft Comput 11:399–409

    Article  Google Scholar 

  • 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 122:349–369

    Article  Google Scholar 

  • 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 18:471–493

    Article  Google Scholar 

  • 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 25:747–759

    Article  Google Scholar 

  • 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 48:4164–4177

    Article  Google Scholar 

  • Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer Science & Business Media

  • Prosser IP, Rustomji P (2000) Sediment transport capacity relations for overland flow. Prog Phys Geogr 24:179–193

    Article  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. Evol Comput IEEE Trans 13:398–417

    Article  Google Scholar 

  • Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo

    Google Scholar 

  • 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 1-10

  • Ray RL, Jacobs JM (2007) Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat Hazards 43:211–222

    Article  Google Scholar 

  • Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain J Sci 5:1–4

    Google Scholar 

  • Siegel FR (2015) Countering 21st century social-environmental threats to growing global populations. Springer, Berlin

    Book  Google Scholar 

  • Sim J, Kim S-Y, Lee J (2005) Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method. Bioinformatics 21:2844–2849

    Article  Google Scholar 

  • 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) 102:90–99

    Google Scholar 

  • 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 148:409–419

    Article  Google Scholar 

  • 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 2012:1–26

    Article  Google Scholar 

  • 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 382-389

  • 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 171–172:12–29

    Article  Google Scholar 

  • 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 45:199–211

    Article  Google Scholar 

  • 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 96:28–40

    Article  Google Scholar 

  • 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 303–317

    Google Scholar 

  • 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 66:707–730

    Article  Google Scholar 

  • 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 87–111

    Chapter  Google Scholar 

  • 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 

  • 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

  • Tsangaratos P, Ilia I (2015) Landslide susceptibility mapping using a modified decision tree classifier in the xanthi perfection, Greece. Landslides 1-16

  • 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 107:26–34

    Article  Google Scholar 

  • 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 101:572–582

    Article  Google Scholar 

  • 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 1-18

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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.

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Tien Bui, D., Nguyen, Q.P., Hoang, ND. et al. 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. Landslides 14, 1–17 (2017). https://doi.org/10.1007/s10346-016-0708-4

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Keywords

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