Abstract
The aim of this study is to investigate potential applications of multi-layer perceptron neural networks (MLP Neural Nets) and radial basis function neural networks (RBF Neural Nets) for landslide susceptibility mapping in the Yihuang area (China). First, a landslide inventory map with 187 landslide locations was generated, and then the map was randomly partitioned into a ratio of 70/30 for training and validating models. Second, 14 landslide conditioning factors (slope, altitude, aspect, topographic wetness, sediment transport index (STI), stream power index (SPI), plan curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), lithology, rainfall) were prepared. Using MLP Neural Nets and RBF Neural Nets, two landslide susceptibility models were constructed and two landslide susceptibility maps were generated. Finally, the two resulting landslide susceptibility maps were validated using the landslide locations and the receiver operating characteristic (ROC) method. The validation results showed that the areas under the ROC curve (AUC) for the two landslide models produced by MLP Neural Nets and RBF Neural Nets are 0.932 and 0.765 for success rate curve and 0.757 and 0.725 for prediction rate curve, respectively. The results showed that the MLP Neural Nets model is better than the RBF Neural Nets model in this study. The results may be useful for general land use planning and hazard mitigation purposes.
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References
Arıkan F, Ulusay R, Aydın N (2007) Characterization of weathered acidic volcanic rocks and a weathering classification based on a rating system. Bull Eng Geol Environ 66(4):415–430
Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385
Bagher-Ebadian H, Jafari-Khouzani K, Mitsias PD, Lu M, Soltanian-Zadeh H, Chopp M, Ewing JR (2011) Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke. PLoS ONE 6(8):e22626. doi:10.1371/journal.pone.0022626
Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5(6):853–862
Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343
Gil D, Johnsson M (2010) Supervised SOM based architecture versus multilayer perceptron and RBF networks. In: Proceedings of the linköping electronic conference, pp 15–24
Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299
Harp EL, Reid ME, McKenna JP, Michael JA (2009) Mapping of hazard from rainfall-triggered landslides in developing countries: examples from Honduras and Micronesia. Eng Geol 104(3–4):295–311
Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River
Hungr O, Fell R, Couture R, Eberhardt E (2005) Landslide risk management. CRC Press, Boca Raton
Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data journals. Int J Remote Sens 26(7):1477–1491
Lee S, Ryu JH, Min KD, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Land 28(12):1361–1376
Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P (2004) Landslide inventories and their statistical properties. Earth Surf Proc Land 29(6):687–711
Melchiorre C, Castellanos Abella EA, van Westen CJ, Matteucci M (2011) Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba. Comput Geosci 37(4):410–425
Pavel M, Fannin RJ, Nelson JD (2008) Replication of a terrain stability mapping using an artificial neural network. Geomorphology 97(3–4):356–373
Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054
Pradhan B, 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. Environ Model Softw 25(6):747–759
Pradhan B, Lee S (2010c) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30
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(3):216–235
Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010b) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177
Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology 109(3–4):108–121
Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219
Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199. doi:10.1016/j.cageo.2011.09.011
Spiker EC, Gori P (2003) National landslide hazards mitigation strategy, a framework for loss reduction. US Geological Survey, Reston
Tien Bui D, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444
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. doi:10.1155/2012/974638
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. In: Proceedings of the iEMSs sixth biennial meeting: international congress on environmental modelling and software (iEMSs 2012), International Environmental Modelling and Software Society, Leipzig, Germany
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
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
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
Tien Bui D, Ho TC, Revhaug I, Pradhan B, Nguyen D (2013) 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
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 International Publishing, Berlin, pp 87–111
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. doi:10.1007/s10346-015-0557-6
Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114
Were K, Tien Bui D, Dick ØB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Ind 52:394–403
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(4):572–582
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(6):1125–1138
Yilmaz I (2010a) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836
Yilmaz I (2010b) The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60(3):505–519
Zare M, Pourghasemi H, 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(8):2873–2888
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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: Robbi Sluter, C., Madureira Cruz, C., Leal de Menezes, P. (eds) Cartography - Maps Connecting the World. Lecture Notes in Geoinformation and Cartography(). Springer, Cham. https://doi.org/10.1007/978-3-319-17738-0_13
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