[go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Aguiar-Pulido V, Munteanu CR, Seoane JA, Fernández-Blanco E, Pérez-Montoto LG, González-Díaz H, Dorado J (2012) Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer. Mol BioSyst 8:1716–1722

    Article  Google Scholar 

  • Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106

    Article  Google Scholar 

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34

    Article  Google Scholar 

  • Aksoy B, Ercanoglu M (2012) Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey). Comput Geosci 38:87–-98

    Article  Google Scholar 

  • Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province, Iran. Catena 120:149–162

    Article  Google Scholar 

  • 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 (2005a) 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 

  • Bagchi D (2011) Ground Water Brochure, District Tehri Garhwal, Uttarakhand. Central Ground Water Board. Retrieved April 2014.

  • Bai S, Lü G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang. China Environ Earth Sci 62:139–149

    Article  Google Scholar 

  • Basheer I, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. Journal of microbiological methods 43:3–31

    Article  Google Scholar 

  • Benediktsson J, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data Geoscience and Remote Sensing. IEEE Trans 28:540–552

    Google Scholar 

  • Bhargavi P, Jyothi S (2009) Applying naive Bayes data mining technique for classification of agricultural land soils. Int J Comput Sci Netw Secur 9:117–122

    Google Scholar 

  • Breiman L (1996) Bagging Predictors Machine Learning 24:123–140

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

    Article  Google Scholar 

  • Cascini G, Zini M (2008) Measuring patent similarity by comparing inventions functional trees. In: Computer-aided innovation (CAI). Springer, pp 31-42

  • Chen G, Li X, Chen J, Yn Z, Peijnenburg WJ (2014) Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression. Environmental Toxicology and Chemistry 33:2688–2693

    Article  Google Scholar 

  • Chen H, Liu H, Han J, Yin X, He J (2009) Exploring optimization of semantic relationship graph for multi-relational Bayesian classification Decision Support Systems, Elsevier 48:112–121

  • Chen J, Zeng Z, Jiang P, Tang H (2015) Deformation prediction of landslide based on functional network. Neurocomputing 149(Part A):151–157

    Article  Google Scholar 

  • Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology 124:12–23

    Article  Google Scholar 

  • Chung C-JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Natural Hazards 30:451-472

  • Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). CATENA 113:236–250

    Article  Google Scholar 

  • Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Märker M (2014) Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology 204:399–411

    Article  Google Scholar 

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • Dai J, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Applied Soft Computing 13:211–221

    Article  Google Scholar 

  • Das I, Sahoo S, van Westen C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114:627–637

    Article  Google Scholar 

  • Dash M, Liu H (1997) Feature selection for classification. Intell data Anal 1:131–156

    Article  Google Scholar 

  • Dash M, Liu H (2003) Consistency-based search in feature selection. Artificial Intelligence 151:155–176

    Article  Google Scholar 

  • Doshi M, Chaturvedi SK (2014) Correlation based feature selection (CFS) technique to predict student performance. International Journal of Computer Networks & Communication (UCNC) 6

  • Dou J et al. (2015a) 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 

  • Dou J, Chang K-T, Chen S, Yunus AP, Liu J-K, Xia H, Zhu Z (2015b) Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sens 7:4318–4342

    Article  Google Scholar 

  • Dou J, Oguchi T, Hayakawa YS, Uchiyama S, Saito H, Paudel U (2014) GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan. In: Landslide science for a safer geoenvironment. Springer, pp 419-424

  • Dou J, Paudel U, Oguchi T, Uchiyama S, Hayakavva YS (2015c) Shallow and Deep-Seated Landslide Differentiation Using Support Vector Machines: A Case Study of the Chuetsu Area. Terrestrial, Atmospheric & Oceanic Sciences, Japan, p. 26

    Google Scholar 

  • Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y, Zhu Z (2015d) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Natural Hazards:1-28

  • Fabbri AG, Chung CF, Napolitano P, Remondo J, Zêzere JL (2002) Prediction rate functions of landslide susceptibility applied in the Iberian Peninsula

  • Feizizadeh B, Jankowski P, Blaschke T (2014) A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Comput Geosci 64:81–95

    Article  Google Scholar 

  • Fourniadis I, Liu J, Mason P (2007) Regional assessment of landslide impact in the Three Gorges area. China using ASTER data. Wushan-Zigui Landslides 4:267–278

    Article  Google Scholar 

  • Gama J (2000) A linear-bayes classifier. In: Advances in artificial intelligence. Springer, pp 269-279

  • Gama J (2004) Functional trees. Machine Learning 55:219–250

    Article  Google Scholar 

  • Gaprindashvili G, Guo J, Daorueang P, Xin T, Rahimy P (2014) A new statistic approach towards landslide hazard risk assessment. Int J Geosci 5:38–49

    Article  Google Scholar 

  • Garcia-Rodriguez MJ, Malpica JA, Benito B, Diaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95:172–191

    Article  Google Scholar 

  • Gardner M, Dorling S (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment 32:2627–2636

    Article  Google Scholar 

  • Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “Antecedent Daily Rainfall Model”. Pure Appl Geophys 157:1059–1079

  • Guha-Sapir D, Hoyois P, Below R (2014) Annual Disaster Statistical Review 2013: The Numbers and Trends. CRED, Brussels

    Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learning Res 3:1157–1182

    Google Scholar 

  • Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272-299

  • Heinimäki TJ, Elomaa T 2013 Facilitating technology forestry: software tool support for creating functional technology trees. In: Proceedings of the 3rd International Conference on Innovative Computing Technology (INTECH’13), pp 510-519

  • 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. Lecture notes in geoinformation and cartography. Springer International Publishing, pp 175-188.

  • Irigaray C, Fernández T, El Hamdouni R, Chacón J (2007) Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Natural Hazards 41:61-79

  • Jebur MN, Pradhan B, Tehrany MS (2015) Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 8:674–690

    Article  Google Scholar 

  • Kapur A, Kapur A, Virji-Babul N, Tzanetakis G, Driessen PF (2005) Gesture-based affective computing on motion capture data. In: Affective computing and intelligent interaction. Springer, pp 1-7

  • Karegowda AG, Manjunath A, Jayaram M (2010) Comparative study of attribute selection using gain ratio and correlation based feature selection. Int J Inf Technol Knowledge Manag 2:271–277

    Google Scholar 

  • Kavzoglu T, Kutlug Sahin E, Colkesen I (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Eng Geol 192:101–112

    Article  Google Scholar 

  • Kavzoglu T, Sahin EK, 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 

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

    Article  Google Scholar 

  • Lan H, Frank E, Hall M (2011) Data mining: Practical machine learning tools and techniques. Morgan Kaufman, Boston

    Google Scholar 

  • Lee S, Ryu J-H, Won J-S, Park H-J (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Lepora NF, Evans M, Fox CW, Diamond ME, Gurney K, Prescott TJ 2010 Naive Bayes texture classification applied to whisker data from a moving robot. In: Neural networks (IJCNN), The 2010 International Joint Conference on, IEEE, pp 1-8

  • Li Y, Chen G, Tang C, Zhou G, Zheng L (2012) Rainfall and earthquake-induced landslide susceptibility assessment using GIS and artificial neural network. Natural Hazards and Earth System Sciences

  • Lin S-W, Lee Z-J, Chen S-C, Tseng T-Y (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8:1505–1512

    Article  Google Scholar 

  • Lu S-H, Chiang D-A, Keh H-C, Huang H-H (2010) Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values. Knowl-Based Syst 23:598–604

    Article  Google Scholar 

  • Marjanovic M, Kovacevic M, Bajat B, Vozenílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234

    Article  Google Scholar 

  • Martha TR, van Westen CJ, Kerle N, Jetten V, Vinod Kumar K (2013) Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology 184:139–150

    Article  Google Scholar 

  • Melchiorre C, Matteucci M, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400

  • Mladenić D, Brank J, Grobelnik M, Milic-Frayling N 2004 Feature selection using linear classifier weights: interaction with classification models. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 234-241

  • 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. Journal of Asian Earth Sciences 61:221–236

    Article  Google Scholar 

  • Murata N, Yoshizawa S, Amari S-i (1994) Network information criterion-determining the number of hidden units for an artificial neural network model. Neural Networks. IEEE Transac 5:865–872

    Google Scholar 

  • NCEP (2014) Global Weather data for SWAT http://globalweather.tamu.edu/home/view13292

  • Nerini D, Ghattas B (2007) Classifying densities using functional regression trees: Applications in oceanology. Computational Statistics & Data Analysis 51:4984–4993

    Article  Google Scholar 

  • Oh H-J, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences 37:1264–1276

    Article  Google Scholar 

  • Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343

    Article  Google Scholar 

  • Onagh M, Kumra V, Rai PK (2012) Landslide susceptibility mapping in a part of Uttarkashi district (India) by multiple linear regression method. International Journal of Geology, Earth and Environmental Sciences ISSN:2277-2081

  • Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences 64:180–197

    Article  Google Scholar 

  • Pareek N, Pal S, Sharma ML, Arora MK (2013) Study of effect of seismic displacements on landslide susceptibility zonation (LSZ) in Garhwal Himalayan region of India using GIS and remote sensing techniques. Comput Geosci 61:50–63

    Article  Google Scholar 

  • Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology 204:287–301

    Article  Google Scholar 

  • Polemio M, Petrucci O (2000) Rainfall as a landslide triggering factor an overview of recent international research. Landslides in research, theory and practice

  • Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013a) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 2:349–369

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013b) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian J Geosci 6:2351–2365

    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. Environmental and Ecological Statistics 18:471–493

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–-365

    Article  Google Scholar 

  • Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042

    Article  Google Scholar 

  • Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  • Pradhan B, Lee S, Buchroithner MF (2010a) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34:216–235

    Article  Google Scholar 

  • Pradhan B, Sezer EA, Gokceoglu C, Buchroithner FM (2010b) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). Transactions on Geoscience and Remote Sensing

  • Prandini L, Guidiini G, Bottura J, Pançano W, Santos A (1977) Behavior of the vegetation in slope stability: a critical review. Bulletin of Engineering Geology and the Environment 16:51-55

  • Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in CCentral Nepal Himalaya. Arabian Journal of Geosciences 7:725–742

    Article  Google Scholar 

  • Rosen GL, Reichenberger ER, Rosenfeld AM (2011) NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads. Bioinformatics 27:127–129

    Article  Google Scholar 

  • Saboya Jr F, da Glória AM, Dias Pinto W (2006) Assessment of failure susceptibility of soil slopes using fuzzy logic. Eng Geol 86:211–224

    Article  Google Scholar 

  • Sadr MP, Maghsoudi A, Saljoughi BS (2014) Landslide susceptibility mapping of Komroud sub-basin using fuzzy logic approach. Geodynamics Research International Bulletin 2

  • Sarkar S, Kanungo D, Chauhan P (2011) Varunavat landslide disaster in Uttarkashi, Garhwal Himalaya, India. Q J Eng Geol Hydrogeol 44:17–22

    Article  Google Scholar 

  • Sasikala S, alias Balamurugan SA, Geetha S (2014) Multi filtration feature selection (MFFS) to improve discriminatory ability in clinical data set. Applied Computing and Informatics

  • Sassa K, Canuti P (2008) Landslides—disaster risk reduction. Springer Science & Business Media,

  • Schicker R, Moon V (2012) Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161–162:40–57

    Article  Google Scholar 

  • Scolobig A, Pelling M (2015) The co-production of risk from a natural hazards perspective science and policy interaction for landslide risk management in Italy

  • Şenkal O, Kuleli T (2009) Estimation of solar radiation over Turkey using artificial neural network and satellite data. Appl Energy 86:1222–1228

    Article  Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2013) Erratum to: “Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia” [Expert Systems with Applications 38 (2011) 8208–8219]. Expert Syst Appl 40:2360

    Article  Google Scholar 

  • Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70

    Article  Google Scholar 

  • Singhroy V, Mattar K, Gray A (1998) Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images. Adv Space Res 21:465–476

    Article  Google Scholar 

  • Soni J, Ansari U, Sharma D, Soni S (2011) Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int J Comput Appl 17:43–48

    Google Scholar 

  • Soria D, Garibaldi JM, Biganzoli E, Ellis IO (2008) A comparison of three different methods for classification of breast cancer data. In: Machine learning and applications. In: ICMLA ’08. Seventh International Conference on, 2008. IEEE, pp. 619–624

    Google Scholar 

  • Stocking M (1972) Relief analysis and soil erosion in Rhodesia using multi-variate techniques. Zeitschrift fur Geomorphologie NF 16:432–-443

    Google Scholar 

  • Tien Bui D, Chung Ho T, Revhaug I, Pradhan B, Ba Nguyen D (2014a) Landslide susceptibility mapping along the National Road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. Geoinformation and Cartography

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012a) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering 2012

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, B Dick O (2012b) Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis. International Environmental Modelling and Software Society (iEMSs)

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012c) 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 (2012d) 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, Pradhan B, Revhaug I, Trung Tran C (2014b) 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. Remote Sensing Applications in Environmental Research

  • 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: 1–18

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Computers & Geosciences 36:1101–1114

  • Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerckhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410

  • Vijith H, Madhu G (2008) Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environmental Geology 55:1397–1405

    Article  Google Scholar 

  • Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80

    Article  Google Scholar 

  • Yalcin A, Reis S, Aydinoglu A, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287

  • Yee LP, De Silva LC 2002 Application of multilayer perceptron network as a one-way hash function. In: Neural networks, 2002. IJCNN ‘02. Proceedings of the 2002 International Joint Conference on. IEEE, pp 1459–1462

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6:2873–2888

    Article  Google Scholar 

  • Zhang H, Su J (2004) Naive Bayesian classifiers for ranking machine learning 3201:501–512

  • Zhang W, Gao F (2011) An improvement to naive Bayes for text classification. Procedia Eng 15:2160–2164

    Article  Google Scholar 

Download references

Acknowledgments

This research is funded by the ICCR scholarship program of Indian government to the first author for his Ph.D studies at the Department of Civil Engineering, Gujarat Technological University, Gujarat, India. Authors are also thankful to Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science & Technology, Government of Gujarat, Gandhinagar, Gujarat, India for providing facilities to carry out this research work.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Gujarat Technological University, Nr. Visat Three Roads, Visat - Gandhinagar Highway, Chandkheda, Ahmedabad, Gujarat, 382424, India

    Binh Thai Pham

  2. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Binh Thai Pham

  3. Geographic Information System Group, Department of Business Administration and Computer Science, University College of Southeast Norway, Hallvard Eikas Plass 1, N-3800, Bø i Telemark, Norway

    Dieu Tien Bui

  4. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  5. Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India

    Prakash Indra

  6. Department of Civil Engineering, LDCE, Gujarat Technological University, Ahmedabad, Gujarat, 380015, India

    M. B. Dholakia

Authors
  1. Binh Thai Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dieu Tien Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Reza Pourghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prakash Indra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. B. Dholakia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Binh Thai Pham.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, B.T., Tien Bui, D., Pourghasemi, H.R. et al. 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 128, 255–273 (2017). https://doi.org/10.1007/s00704-015-1702-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-015-1702-9

Keywords

Search

Navigation