This paper uses a probability-based approach to study the spatial relationships between landslides and their causative factors in the Mingchukur area, Bostanlik districts of Tashkent, Uzbekistan. The approach is based on digital databases and incorporates methods including probability analysis, spatial pattern analysis, and interactive mapping. First, an object-oriented conceptual model for describing landslide events is proposed, and a combined database of landslides and environmental factors is constructed by integrating various databases within a unifying conceptual framework. The frequency ratio probability model and landslide occurrence data are linked for interactive, spatial evaluation of the relationships between landslides and their causative factors. In total, 15 factors were analyzed, divided into topography, hydrology, and geology categories. All analyzed factors were also divided into numerical and categorical types. Numerical factors are continuous and were evaluated according to their R2 values. A landslide susceptibility map was constructed based on conditioning factors and landslide occurrence data using the frequency ratio model. Finally, the map was validated and the accuracy showed the satisfactory value of 83.3%.
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Ayalew, L. and 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.
Bates, R.J. and Jackson, J.A., 1984, Dictionary of Geological Terms (3rd edition). American Geological Institute, New York, 299 p.
Buchanan B.P., Fleming, M., Schneider, R.L., Richards, B.K., Archibald, J., Qiu, Z., and Walter, M.T., 2014, Evaluating topographic wetness indices across central New York agricultural landscapes. Hydrology and Earth System Sciences, 18, 3279–3299.
Burrough, P.A., McDonell, R.A., and Lloyd, C.D., 1998, Principles of Geographical Information Systems (3rd edition). Oxford University Press, New York, 190 p.
Cameron, A.C. and Windmeijer F.A.G., 1997, An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics, 77, 329–342.
Carillo, G., Torch, P.A., Sivapalan, M., Wagener, T., Harman, C., and Sawicz, K., 2011, Catchment classification: hydrological analysis of catchment behaviour through process-based modelling along a climate gradient. Hydrology and Earth System Science, 15, 3411–3430.
Chen, W., Pourghasemi, H.R., and Naghibi, S.A., 2018a, A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77, 647–664.
Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Tien Bui, D., Duan, Z., and Ma, J., 2017, A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160.
Chen, W., Zhang, S., Li, R., and Shahabi, H., 2018b, Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naive bayes tree for landslide susceptibility modeling. Science of the Total Environment, 644, 1006–1018.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner J., 2015, System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8, 1991–2007.
Cristea, N.C., Breckheimer, I., Raleigh, M.S., HilleRisLambers, J., and Lundquist, J.D., 2017, An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery. Water Resources Research, 53, 6802–6820.
Desmet, P.J.J. and Govers, G., 1996, A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation, 51, 427–433.
Ding, Q., Chen, W., and Hong, H., 2017, Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto International, 32, 619–639.
Dominguez-Cuesra, M.J., Jimenez-Sanchez, M., and Gonzalez-Rogriguez, G., 2010, Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment. International Journal of Earth Sciences, 99, 661–674.
Du, G.L., Zhang, Y.S., Iqbal, J., Yang, Z.H., and Yao, X., 2017, Landslide susceptibility mapping using integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu province, China. Journal of Mountain Science, 14, 249–268.
Freeman, G.T., 1991, Calculating catchment area with divergent flow based on a regular grid. Computers and Geosciences, 17, 413–422
Hong, H., Pourghasemi, H.R., and Pourtaghi, Z.S., 2016, Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology, 259, 105–118.
Irigaray, C., Fernandez, T., El Hamdouni, R., and Chacon, 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.
Jeff, S.J., 2004, Calculating landscape surface area from digital elevation models. Wildlife Society Bulletin, 32, 829–839.
Kim, J.C., Jung, H.S., and Lee, S., 2018, Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural networks: focus on topographic factors. Journal of Hydroinformatics. https://doi.org/10.2166/hydro.2018.120
Kim, J.C., Sunmin, L., Jung, H.S., and Lee, S., 2018, Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto International, 33, 1000–1015.
Kvalseth, T.O., 1985, Cautionary Note about R2. The American Statistician, 39, 279–285.
Lee, M.J., Park, I., and Lee, S., 2015, Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neurofuzzy models: a case study of Seorak mountain area in Korea. Environmental Earth Sciences, 74, 413–429.
Lee, M.J., Park, I., Won, J.S., and Lee, S., 2016, Landslide hazard mapping considering rainfall probability in Inje, Korea. Geomatics, Natural Hazards and Risk, 7, 424–446.
Lee, S. and Lee, M.J., 2017, Susceptibility mapping of Umyeonsan using logistic regression (LR) model and post-validation through field investigation. Korean Journal of Remote Sensing, 33, 1047–1060.
Lee, S. and Park, I., 2013, Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. Journal of Environmental Management, 127, 166–176.
Lee, S. and Pradhan, B., 2007, Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression model. Landslides, 4, 33–41.
Lee, S. and Talib, J.A., 2005, Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.
Lee, S., Hong, S.M., and Jung, H.S., 2017, A support vector machine for landslide susceptibility mapping in Gangwon province, Korea. Sustainability, 9, 48.
Lee, S., Jeon, S.W., Oh, K.Y., and Lee, M.J., 2016, The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: a case study of Inje, Korea. Open Geosciences, 8, 117–132.
Lee, S., Lee, M.J., and Jung, H.S., 2017, Data mining approaches for landslide susceptibility mapping in Umyeonsan, Seoul, South Korea. Applied Sciences, 7, 683.
Lee, S., Lee, S., Lee, M.J., and Jung, H.S., 2018, Spatial assessment of urban flood susceptibility using data mining and geographic information System (GIS) tools. Sustainability, 10, 648.
Lee, S., Won, J.S., Jeon, S.W., Park, I., and Lee, M.J., 2015, Spatial landslide hazard prediction using rainfall probability and a logistic regression model. Mathematical Geosciences, 47, 565–589.
Mezaal, M.R. and Pradhan, B., 2018, Data mining-aided automatic landslide detection airborne laser scanning data in densely forested tropical areas. Korean Journal of Remote Sensing, 34, 45–74.
Moore, I.D., Grayson, R.B., and Landson, A.R., 1991, Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5, 3–30.
Oh, H.J., Lee, S., and Hong, S.M., 2017, Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. Journal of Sensors, 2017, 21.
Oh, C.Y., Kim, K.T., and Chou, C.U., 2009, Analysis of landslide characteristics of Inje area using SPOT5 image and GIS analysis. Korean Journal of Remote Sensing, 25, 445–454.
Oh, H.J. and Lee, S., 2017, Shallow landslide susceptibility modeling using the data mining models artificial neural network and boosted tree. Applied Sciences, 7, 1000.
Oh, H.J., 2010, Landslide detection and landslide susceptibility mapping using aerial photos and artificial neural network. Korean Journal of remote sensing, 26, 47–57.
Park, I. and Lee, S., 2014, Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea. International Journal of Remote Sensing, 35, 6089–6112.
Park, N.W. and Kyriakidis, P.C., 2008, Gestatistical integration of different sources of elevation and its effect on landslide hazard mapping. Korean Journal of Remote Sensing, 24, 453–462.
Pham, B.T., Tien Bui, D., Pourghasemi, H.R., Indra, P., and Dholakia, M.B., 2017, Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a compariosn study of prediction capability of naive bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology, 128, 255–273.
Polykretis, C. and Chalkias, C., Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network. Natural Hazards, 93, 249–274.
Pradhan, B. and Lee, S., 2010a, Delineation of landslide hazard areas using frequency ratio, logistic regression, and artificial neural network model at Penang Island, Malaysia. Environmental Earth Sciences, 60, 1037–1054.
Pradhan, B. and Lee, S., 2010b, Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software, 25, 747–759.
Quinn, P., Beven, K., Chevallier, P., and Planchon, O., 1991, The prediction of hill-slope flow paths for distributed hydrological modelling using digital terrain models. Hydrological Processes, 5, 59–79.
Rakhmatullaev, S., Huneau, F., Celle-Jeanton, H., Le Coustumer, P., Motelica-Heino, M., and Bakiev, M., 2013, Water reservoirs, irrigation and sedimentation in Central Asia: a first-cut assessment for Uzbekistan. Environmental Earth Sciences, 68, 985–998.
Regmi, A.D., Devkota, K.C., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Kumamoto, T., and Akgun, A., 2014, Application of frequency ratio, statistical index, and weight of evidence models and their comparison in landslide susceptibility mapping in central Nepal Himalaya. Arabian Journal of Geosciences, 7, 725–742.
Tien Bui, D., Tuan, T.A., Hoang, N.D., Thanh, N.Q., Nguyen, D.B., Liem, N.V., and Pradhan, B., 2017, Spatial prediction of rainfallinduced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14, 447–458.
Truong, X.L., Mitamura, M., Kono, Y., Raghavan, V., Yonezawa, G., Truong, X.Q., Hang Do, T., Tien Bui, D., and Lee, S., 2018, Enhancing prediction performance of landslide susceptibility model using hybrid machine learning approach of bagging ensemble and logistic model tree. Applied Sciences, 8, 1046.
Wilson, J.P. and Gallant, J.C., 2000, Terrain Analysis: Principles and Applications. John Wiley and Sons, Inc., New York, 479 p.
Youssef, A.M., Al-Kathery, M., and Pradhan, B., 2015, Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosciences Journal, 19, 113–134.
Zhu, L. and Huang, J.F., 2006, GIS-based logistic regression method for landslide susceptibility mapping in regional scale. Journal of Zhejiang University-SCIENCE A, 7, 2007–2017.
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Kadirhodjaev, A., Kadavi, P.R., Lee, C. et al. Analysis of the relationships between topographic factors and landslide occurrence and their application to landslide susceptibility mapping: a case study of Mingchukur, Uzbekistan. Geosci J 22, 1053–1067 (2018). https://doi.org/10.1007/s12303-018-0052-x
- landslide susceptibility
- frequency ratio