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Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier

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Abstract

In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naïve Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management.

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References

  • 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(1), 93–106.

    Article  Google Scholar 

  • Alkhasawneh, M Sh, Ngah, U. K., Tay, L. T., Isa, N. A. M., & Al-Batah, M. S. (2014). Modeling and testing landslide hazard using decision tree. Journal of Applied Mathematics. https://doi.org/10.1155/2014/929768.

    Google Scholar 

  • Atkinson, P. M., & Massari, R. (2011). Autologistic modelling of susceptibility to landsliding in the Central Apennines, Italy. Geomorphology, 130(1–2), 55–64. https://doi.org/10.1016/j.geomorph.2011.02.001.

    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(1), 15–31.

    Article  Google Scholar 

  • Bai, S., Lü, G., Wang, J., Zhou, P., & Ding, L. (2011). GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environmental Earth Sciences, 62(1), 139–149. https://doi.org/10.1007/s12665-010-0509-3.

    Article  Google Scholar 

  • Bennett, N. D., Croke, B. F., Guariso, G., Guillaume, J. H., Hamilton, S. H., Jakeman, A. J., et al. (2013). Characterising performance of environmental models. Environmental Modelling and Software, 40, 1–20.

    Article  Google Scholar 

  • Catani, F., Lagomarsino, D., Segoni, S., & Tofani, V. (2013). Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Natural Hazards and Earth System Sciences, 13, 2815–2831.

    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. https://doi.org/10.1016/j.enggeo.2011.09.011.

    Article  Google Scholar 

  • Chung, C.-J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472.

    Article  Google Scholar 

  • Dai, F., Lee, C., Li, J., & Xu, Z. (2001). Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environmental Geology, 40(3), 381–391.

    Article  Google Scholar 

  • Dang, V. H., Dieu, T. B., Tran, X. L., & Hoang, N. D. (2018). Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-018-1273-y.

    Google Scholar 

  • Egan, J. P. (1975). Signal detection theory and ROC analysis. New York: Academic Press.

  • Felicísimo, Á. M., Cuartero, A., Remondo, J., & Quirós, E. (2013). Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study. Landslides, 10(2), 175–189.

    Article  Google Scholar 

  • Flores, M. J., Gámez, J. A., Martínez, A. M., & Salmerón, A. (2011). Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. In 2011 11th International conference on intelligent systems design and applications (ISDA) (pp. 593–598). IEEE.

  • Gian, Q. A., Tran, D. T., Nguyen, D. C., Nhu, V. H., & Tien Bui, D. (2017). Design and implementation of site-specific rainfall-induced landslide early warning and monitoring system: a case study at Nam Dan landslide (Vietnam). Geomatics, Natural Hazards and Risk, 8(2), 1978–1996.

    Article  Google Scholar 

  • Gomez, H., & Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78(1), 11–27.

    Article  Google Scholar 

  • Guzzetti, F. (2006). landslide hazard and risk assessment. Bonn: University of Bonn.

    Google Scholar 

  • Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1), 181–216. https://doi.org/10.1016/S0169-555X(99)00078-1.

    Article  Google Scholar 

  • Hoang, N. D., & Tien Bui, D. (2018). Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: a case study in Vietnam. Natural Hazards, 92(3), 1871–1887.

    Article  Google Scholar 

  • Hong, H., Xu, C., Revhaug, I., & Bui, D. T. (2015). 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 Cartography-maps connecting the world (pp. 175–188). Springer.

  • Hong, H., Pradhan, B., Bui, D. T., Xu, C., Youssef, A. M., & Chen, W. (2016). Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomatics, Natural Hazards and Risk, 8(2), 544–569.

    Article  Google Scholar 

  • Htike, Z. Z., & Win, S. L. (2013a). Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution. Procedia Computer Science, 23, 36–43.

    Article  Google Scholar 

  • Htike, Z. Z., & Win, S. L. (2013b). Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators. Procedia Computer Science, 23, 60–67.

    Article  Google Scholar 

  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.

    Article  Google Scholar 

  • Jaafari, A., Najafi, A., Pourghasemi, H., Rezaeian, J., & Sattarian, A. (2014). GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4), 909–926.

    Article  Google Scholar 

  • Jaafari, A., Rezaeian, J., & Omrani, M. S. O. (2017). Spatial prediction of slope failures in support of forestry operations safety. Croatian Journal of Forest Engineering, 38(1), 107–118.

    Google Scholar 

  • Kamp, U., Growley, B. J., Khattak, G. A., & Owen, L. A. (2008). GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology, 101(4), 631–642.

    Article  Google Scholar 

  • Kavzoglu, T., & Colkesen, I. (2013). An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping. International Journal of Remote Sensing, 34(12), 4224–4241. https://doi.org/10.1080/01431161.2013.774099.

    Article  Google Scholar 

  • Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425–439.

    Article  Google Scholar 

  • Koc, L., Mazzuchi, T. A., & Sarkani, S. (2012). A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier. Expert Systems with Applications, 39(18), 13492–13500.

    Article  Google Scholar 

  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.

    Article  Google Scholar 

  • Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491.

    Article  Google Scholar 

  • Lee, S., & Oh, H.-J. (2012). Ensemble-based landslide susceptibility maps in Jinbu area, Korea. In B. Pradhan & M. Buchroithner (Eds.), Terrigenous mass movements (pp. 193–220). Berlin: Springer.

    Chapter  Google Scholar 

  • Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33–41.

    Article  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. Engineering Geology, 71(3), 289–302.

    Article  Google Scholar 

  • Lucà, F., Conforti, M., & Robustelli, G. (2011). Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology, 134(3–4), 297–308.

    Article  Google Scholar 

  • Moreiras, S. M. (2006). Frequency of debris flows and rockfall along the Mendoza river valley (Central Andes), Argentina: Associated risk and future scenario. Quaternary International, 158(1), 110–121.

    Article  Google Scholar 

  • Mujalli, R. O., López, G., & Garach, L. (2016). Bayes classifiers for imbalanced traffic accidents datasets. Accident Analysis and Prevention, 88, 37–51.

    Article  Google Scholar 

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

  • Nefeslioglu, H. A., 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. Engineering Geology, 97(3–4), 171–191. https://doi.org/10.1016/j.enggeo.2008.01.004.

    Article  Google Scholar 

  • Nefeslioglu, H., Sezer, E., Gokceoglu, C., Bozkir, A., & Duman, T. (2010). Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering, 2010. https://doi.org/10.1155/2010/901095.

  • Ohlmacher, G. C., & Davis, J. C. (2003a). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69(3–4), 331–343. https://doi.org/10.1016/S0013-7952(03)00069-3.

    Article  Google Scholar 

  • Ohlmacher, G. C., & Davis, J. C. (2003b). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology, 69(3), 331–343.

    Article  Google Scholar 

  • Onagh, M., Kumra, V., & Rai, P. K. (2012). Landslide susceptibility mapping in a part of Uttarkashi district (India) by multiple linear regression method. International Journal of Geology, Earth and Environmental Sciences, 2, 102–120. ISSN 2277-2081.

    Google Scholar 

  • Pham, B. T., Bui, D. T., Dholakia, M. B., Prakash, I., Pham, H. V., Mehmood, K., et al. (2016a). A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomatics, Natural Hazards and Risk. https://doi.org/10.1080/19475705.2016.1255667.

    Google Scholar 

  • Pham, B. T., Bui, D. T., Prakash, I., & Dholakia, M. (2016b). Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS. Journal of Geomatics, 10(1), 71–79.

    Google Scholar 

  • Pham, B. T., Tien Bui, D., Dholakia, M. B., Prakash, I., & Pham, H. V. (2016c). 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. Geotechnical and Geological Engineering, 34(1), 1–18. https://doi.org/10.1007/s10706-016-9990-0.

    Article  Google Scholar 

  • Pham, B. T., Tien Bui, D., Indra, P., & Dholakia, M. (2015a). Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency ratio method. International Journal of Engineering Research and Technology, 4, 338–344.

    Google Scholar 

  • Pham, B. T., Tien Bui, D., Pham, H. V., Le, H. Q., Prakash, I., & Dholakia, M. B. (2016d). Landslide hazard assessment using random subspace fuzzy rules based classifier ensemble and probability analysis of rainfall data: A case study at Mu Cang Chai District, Yen Bai Province (Viet Nam). Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-016-0620-3.

    Google Scholar 

  • Pham, B. T., Tien Bui, D., Pourghasemi, H. R., Indra, P., & Dholakia, M. B. (2015b). 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. Theoretical and Applied Climatology, 122(3–4), 1–19. https://doi.org/10.1007/s00704-015-1702-9.

    Google Scholar 

  • Pham, B. T., Tien Bui, D., Prakash, I., & Dholakia, M. B. (2016e). Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Natural Hazards, 83(1), 1–31. https://doi.org/10.1007/s11069-016-2304-2.

    Article  Google Scholar 

  • Pham, B. T., Bui, D. T., & Prakash, I. (2018a). Bagging based Support Vector Machines for spatial prediction of landslides. Environmental Earth Sciences, 77(4), 146. https://doi.org/10.1007/s12665-018-7268-y.

    Article  Google Scholar 

  • Pham, B. T., Jaafari, A., Prakash, I., & Bui, D. T. (2018b). A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment. https://doi.org/10.1007/s10064-018-1281-y.

    Google Scholar 

  • Poudyal, C. P., Chang, C., Oh, H.-J., & Lee, S. (2010). Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya. Environmental Earth Sciences, 61(5), 1049–1064.

    Article  Google Scholar 

  • Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., & Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 2, 349–369.

    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. Computers & Geosciences, 51, 350–365.

    Article  Google Scholar 

  • Quinlan, J. R. (1993). C4. 5: Programming for machine learning (p. 38). Morgan Kauffmann: Burlington.

    Google Scholar 

  • Sassa, K., & Canuti, P. (2008). Landslides-disaster risk reduction. Berlin: Springer.

    Google Scholar 

  • Shirzadi, A., Bui, D. T., Pham, B. T., Solaimani, K., Chapi, K., Kavian, A., et al. (2017). Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences, 76(2), 60.

    Article  Google Scholar 

  • Sidle, R. C., & Ochiai, H. (2006). Landslides: Processes, prediction, and land use (Vol. 18). Washington, D.C.: American Geophysical Union.

    Book  Google Scholar 

  • Tait, A., Henderson, R., Turner, R., & Zheng, X. (2006). Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface. International Journal of Climatology, 26(14), 2097–2115.

    Article  Google Scholar 

  • Tien Bui, D., Ho, T.-C., Pradhan, B., Pham, B.-T., Nhu, V.-H., & Revhaug, I. (2016a). GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environmental Earth Sciences, 75(14), 1–22. https://doi.org/10.1007/s12665-016-5919-4.

    Article  Google Scholar 

  • Tien Bui, D., Pham, B. T., Nguyen, Q. P., & Hoang, N.-D. (2016b). 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. International Journal of Digital Earth, 9(11), 1–21. https://doi.org/10.1080/17538947.2016.1169561.

    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 Naive Bayes Models. In Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/974638.

  • Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012b). Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and naive bayes models. Mathematical Problems in Engineering, 2012, 1–26. https://doi.org/10.1155/2012/974638.

    Article  Google Scholar 

  • Tsangaratos, P., & Ilia, I. (2016). Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. CATENA, 145, 164–179.

    Article  Google Scholar 

  • Van Den Eeckhaut, M., Reichenbach, P., Guzzetti, F., Rossi, M., & Poesen, J. (2009). Combined landslide inventory and susceptibility assessment based on different mapping units: An example from the Flemish Ardennes, Belgium. Natural Hazards and Earth System Sciences, 9(2), 507–521.

    Article  Google Scholar 

  • 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(3–4), 392–410. https://doi.org/10.1016/j.geomorph.2005.12.003.

    Article  Google Scholar 

  • Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer.

    Book  Google Scholar 

  • Varnes, D. J. (1984). Landslide hazard zonation: A review of principles and practice (Vol. 3). Paris: UNESCO.

    Google Scholar 

  • Webb, G. I., Boughton, J. R., & Wang, Z. (2005). Not so naive Bayes: Aggregating one-dependence estimators. Machine Learning, 58(1), 5–24.

    Article  Google Scholar 

  • Wilson, J. P., & Gallant, J. C. (2000). Terrain analysis: Principles and applications. New York: Wiley.

    Google Scholar 

  • Wu, J., & Cai, Z. (2011). Learning averaged one-dependence estimators by attribute weighting. Journal of Information & Computational Science, 8(7), 1063–1073.

    Google Scholar 

  • Xiang, Z.-L., & Kang, D.-K. (2017). Attribute weighting for averaged one-dependence estimators. Applied Intelligence, 46(3), 616–629.

    Article  Google Scholar 

  • Xu, C., Xu, X., Dai, F., & Saraf, A. K. (2012). Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Computers & Geosciences, 46, 317–329. https://doi.org/10.1016/j.cageo.2012.01.002.

    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(3), 274–287.

    Article  Google Scholar 

  • Yao, X., Tham, L., & Dai, F. (2008a). Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572–582.

    Article  Google Scholar 

  • Yao, X., Tham, L. G., & Dai, F. C. (2008b). Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China. Geomorphology, 101(4), 572–582. https://doi.org/10.1016/j.geomorph.2008.02.011.

    Article  Google Scholar 

  • Yeon, Y.-K., Han, J.-G., & Ryu, K. H. (2010). Landslide susceptibility mapping in Injae, Korea, using a decision tree. Engineering Geology, 116(3–4), 274–283. https://doi.org/10.1016/j.enggeo.2010.09.009.

    Article  Google Scholar 

  • 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). Computers & Geosciences, 35(6), 1125–1138.

    Article  Google Scholar 

  • Zare, M., Pourghasemi, H. R., 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. Arabian Journal of Geosciences, 6(8), 2873–2888.

    Article  Google Scholar 

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Acknowledgements

Authors are thankful to the Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India for providing facilities to carry out this research work.

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Authors and Affiliations

  1. Geotechnical Engineering and Artificial Intelligence research group (GEOAI), University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Binh Thai Pham

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

    Indra Prakash

  3. Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran

    Abolfazl Jaafari

  4. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

  5. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Dieu Tien Bui

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  1. Binh Thai Pham
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  4. Dieu Tien Bui
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Pham, B.T., Prakash, I., Jaafari, A. et al. Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier. J Indian Soc Remote Sens 46, 1457–1470 (2018). https://doi.org/10.1007/s12524-018-0791-1

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