Artículos científicos
Double landslide susceptibility assessment based on artificial neural networks and weights of evidence
Paul Goyes-Peñafiel- Alejandra Hernandez-Rojas
Published 2021-01-07
Keywords
- Landslide susceptibility,
- Deep Learning,
- Logistic Regression,
- Weights of Evidence,
- Principal Component Analysis
- Artificial Neural Networks ...More
How to Cite
Goyes-Peñafiel, P., & Hernandez-Rojas, A. (2021). Double landslide susceptibility assessment based on artificial neural networks and weights of evidence. Boletín De Geología, 43(1), 173–191. https://doi.org/10.18273/revbol.v43n1-2021009
Copyright (c) 2021 Boletín de Geología
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Abstract
Landslides are the most frequent natural hazards in tropical regions. They cause serious damages to road infrastructure, human losses and effects on economy. Therefore, a quantitative and reliable evaluation of landslide susceptibility is important for territorial planning and development. In this work, the susceptibility calculation is studied with a low uncertain level through the methodological integration of Weights of Evidence method with Artificial Neural Networks. The first one was used to extract the weighted values from the association of variables and the landslide inventory, and the second one to establish the non-linear relation between the conditioning factors and the punctual landslide inventory obtained through the geologic and geomorphologic study of the Popayan municipality. This produces a double verification allowing to extract the characteristics of categorical and continuous variables to produce more accurate susceptibility relations, avoiding multicollinearity and non-significant factors through the Principal Component Analysis. For studying the influence of variables, two methodological proposals were analyzed, the first one with two variables and the second one with fie explanatory variables. For each one, it was applied Logistic Regression, Multilayer Perceptron, and Deep Neural Network quantitative methods as elements of double verification. The results of each model were assessed by the Receiver Operating Characteristics curves. The Deep Neural Networks got an Area Under the Curve with values of 0.902 and 0.969 for proposals 1 and 2, respectively, overcoming Weights of Evidence and Logistic Regression as quantitative methods.
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References
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Bragagnolo, L.; da Silva, R.V.; Grzybowski, J.M.V. (2020). Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks. Environmental Modelling and Software, 123. https://doi.org/10.1016/j.envsoft.2019.104565
Brownlee, J. (2017). Gentle Introduction to the Adam Optimization Algorithm for Deep Learning. Deep Learning Performance. https://machinelearningmastery.com/adamoptimization-algorithm-for-deep-learning/
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Goodfellow, I.; Bengio, Y.; Courville, A. (2016). Deep Learning. MIT Press.
Gwelo, A. (2019). Principal components to overcome multicollinearity problem. Oradea Journal of Business and Economics, 4(1), 79-91.
He, Q.; Shahabi, H.; Shirzadi, A.; Li, S.; Chen, W.; Wang, N.; Chai, H.; Bian, H.; Ma, J.; Chen, Y.; Wang, X.; Chapi, K.; Ahmad, B.B. (2019). Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Science of the Total Environment, 663, 1-15. https://doi.org/10.1016/j.scitotenv.2019.01.329
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Ilia, I.; Tsangaratos, P.; Koumantakis, I.; Rozos, D. (2010). Application of a bayesian approach in GIS based model for evaluating landslide susceptibility. Case study KIMI area, Euboea, Greece. Bulletin of the Geological Society of Greece, 43(3), 1590-1600. https://doi.org/10.12681/bgsg.11333
Ilia, I.; Tsangaratos, P. (2016). Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides, 13(2), 379-397. https://doi.org/10.1007/s10346-015-0576-3
Kadavi, P.R.; Lee, C.W.; Lee, S. (2019). Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environmental Earth Sciences, 78(4). https://doi.org/10.1007/s12665-019-8119-1
Kingma, D.P.; Ba, J.L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations. San Diego, EE.UU.
Lei, T.C.; Wan, S.; Chou, T.Y.; Pai, H.C. (2011). The knowledge expression on debris flow potential analysis through PCA + LDA and rough sets theory: A case study of Chen-Yu-Lan watershed, Nantou, Taiwan. Environmental Earth Sciences, 63(5), 981-997. https://doi.org/10.1007/s12665-010-0775-0
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Lombardo, L.; Mai, P.M. (2018). Presenting logistic regression-based landslide susceptibility results. Engineering Geology, 244, 14-24. https://doi.org/10.1016/j.enggeo.2018.07.019
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Oh, H.J.; Kadavi, P.R.; Lee, C.W.; Lee, S. (2018). Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomatics, Natural Hazards and Risk, 9(1), 1053-1070. https://doi.org/10.1080/19475705.2018.1481147
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. https://doi.org/10.1016/j.jseaes.2012.12.014
Pamela; Sadisun, I.A.; Arifianti, Y. (2018). Weights of evidence method for landslide susceptibility mapping in Takengon, Central Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 118(1). https://doi.org/10.1088/1755-1315/118/1/012037
Patriche, C.V.; Pirnau, R.; Grozavu, A.; Rosca, B. (2016). A comparative analysis of binary logistic regression and analytical hierarchy process for landslide susceptibility assessment in the Dobrovăț River Basin, Romania. Pedosphere, 26(3), 335-350. https://doi.org/10.1016/S1002-0160(15)60047-9
Regmi, N.R.; Giardino, J.R.; Vitek, J.D. (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115(1-2), 172-187. https://doi.org/10.1016/j.geomorph.2009.10.002
Sagar, B.S.D.; Cheng, Q.; Agterberg, F. (2018). Handbook of Mathematical Geosciences: Fifty Years of IAMG. Springer International Publishing. https://doi.org/10.1007/978-3-319-78999-6
Servicio Geológico Colombiano. (2015). Zonificación geomecánica y de amenaza por movimientos en masa del municipio de Popayán - Cauca.
Servicio Geológico Colombiano. (2017). Guía Metodológica para la Zonificación de Amenaza por Movimientos en Masa Escala 1:25000.
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2018.08.003
Trigila, A.; Iadanza, C.; Esposito, C.; Scarascia-Mugnozza, G. (2015). Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology, 249, 119-136. https://doi.org/10.1016/j.geomorph.2015.06.001
van Westen, C.J. (2002). Weights of evidence modeling for landslide susceptibility mapping. International Institute for Geoinformation Science and Earth Observation (ITC), Netherlands.
van Westen, C.J.; Rengers, N.; Soeters, R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30(3), 399-419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e
van Westen, C.J.; Castellanos, E.; Kuriakose, S.L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3-4), 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010
Varnes, D. (1978). Slope movement types and processes. En: R. Schuster; R. Krizek (eds.). Landslides: analysis and control (pp. 11-33). Transportation Research Board.
Wang, Y.; Fang, Z.; Hong, H. (2019). Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Science of the Total Environment, 666, 975-993. https://doi.org/10.1016/j.scitotenv.2019.02.263
Yang, J.; Song, C.; Yang, Y.; Xu, C.; Guo, F.; Xie, L. (2019). New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China. Geomorphology, 324, 62-71. https://doi.org/10.1016/j.geomorph.2018.09.019
Zhao, Y.; Wang, R.; Jiang, Y.; Liu, H.; Wei, Z. (2019). GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China. Engineering Geology, 259. https://doi.org/10.1016/j.enggeo.2019.105147
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Aditian, A.; Kubota, T.; Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101-111. https://doi.org/10.1016/j.geomorph.2018.06.006
Aristizábal, E.; López, S.; Sánchez, O.; Vásquez, M.; Rincón, F.; Ruiz-Vásquez, D.; Restrepo, S.; Valencia, J.S. (2019). Evaluación de la amenaza por movimientos en masa detonados por lluvias para una región de los Andes colombianos estimando la probabilidad espacial, temporal, y magnitud. Boletín de Geología, 41(3), 85-105. https://doi.org/10.18273/revbol.v41n3-2019004
Asf-Alaska. (2007). Dataset: © JAXA/METI ALOS PALSAR L1.0 2007. Recuperado de ASF DAAC. Consultado el 11 de junio de 2020. https://doi.org/10.5067/NXY378J3DFZQ
Baeza, C.; Corominas, J. (2001). Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms, 26(12), 1251-1263. https://doi.org/10.1002/esp.263
Bragagnolo, L.; da Silva, R.V.; Grzybowski, J.M.V. (2020). Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks. Environmental Modelling and Software, 123. https://doi.org/10.1016/j.envsoft.2019.104565
Brownlee, J. (2017). Gentle Introduction to the Adam Optimization Algorithm for Deep Learning. Deep Learning Performance. https://machinelearningmastery.com/adamoptimization-algorithm-for-deep-learning/
Bui, D.T.; Tuan, T.A.; Klempe, H.; Pradhan, B.; Revhaug, I. (2016). 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, 13(2), 361-378. https://doi.org/10.1007/s10346-015-0557-6
Bui, D.T.; Tsangaratos, P.; Nguyen, V.T.; Van Liem, N.; Trinh, P.T. (2020). Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188. https://doi.org/10.1016/j.catena.2019.104426
Cantarino, I.; Carrion, M.A.; Goerlich, F.; Martinez-Ibañez, V. (2019). A ROC analysis-based classification method for landslide susceptibility maps. Landslides, 16(2), 265-282. https://doi.org/10.1007/s10346-018-1063-4
Carvajal-Perico, J.H. (2012). Propuesta de estandarización de la cartografía geomorfológica en Colombia. Bogotá: Servicio Geológico Colombiano.
Chollet, F. (2018). Deep Learning with Python. Manning.
Corominas, J.; van Westen, C.; Frattini, P.; Cascini, L.; Malet, J.P.; Fotopoulou, S.; Catani, F.; Van Den Eeckhaut, M.; Mavrouli, O.; Agliardi, F.; Pitilakis, K.; Winter, M.G.; Pastor, M.; Ferlisi, S.; Tofani, V.; Hervás, J.; Smith, J.T. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209-263. https://doi.org/10.1007/s10064-013-0538-8
Correa, F.; Gallardo, J.; Muñoz, N.; Pérez, R. (2016). Estudio comparativo basado en métricas para diferentes arquitecturas de navegación reactiva. Ingeniare, 24(1), 46-54. https://doi.org/10.4067/S0718-33052016000100005
Correa-Muñoz, N.A.; Higidio-Castro, J.F. (2017). Determination of landslide susceptibility in linear infrastructure. Case: aqueduct network in Palacé, Popayan (Colombia). Ingeniería e Investigación, 37(2), 17-24. https://doi.org/10.15446/ing.investig.v37n2.59654
Dai, F.C.; Lee, C.F.; Ngai, Y.Y. (2002). Landslide risk assessment and management: an overview. Engineering Geology, 64(1), 65-87. https://doi.org/10.1016/S0013-7952(01)00093-X
DANE. (2019). Resultados Censo Nacional de Población y Vivienda 2018. Consultado el 18 de marzo de 2020. http://microdatos.dane.gov.co/index.php/catalog/643
Decreto 1077 de 2015 [con fuerza de ley]. Por medio del cual se expide el Decreto Único Reglamentario del Sector Vivienda, Ciudad y Territorio. 26 de mayo de 2015. D.O. No. 49523.
Goodfellow, I.; Bengio, Y.; Courville, A. (2016). Deep Learning. MIT Press.
Gwelo, A. (2019). Principal components to overcome multicollinearity problem. Oradea Journal of Business and Economics, 4(1), 79-91.
He, Q.; Shahabi, H.; Shirzadi, A.; Li, S.; Chen, W.; Wang, N.; Chai, H.; Bian, H.; Ma, J.; Chen, Y.; Wang, X.; Chapi, K.; Ahmad, B.B. (2019). Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Science of the Total Environment, 663, 1-15. https://doi.org/10.1016/j.scitotenv.2019.01.329
Hemasinghe, H.; Rangali, R.S.S.; Deshapriya, N.L.; Samarakoon, L. (2018). Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka). Procedia Engineering, 212, 1046-1053. https://doi.org/10.1016/j.proeng.2018.01.135
Hong, H.; Ilia, I.; Tsangaratos, P.; Chen, W.; Xu, C. (2017). A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China. Geomorphology, 290, 1-16. https://doi.org/10.1016/j.geomorph.2017.04.002
Hu, Q.; Zhou, Y.; Wang, S.; Wang, F. (2020). Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin. Geomorphology, 351. https://doi.org/10.1016/j.geomorph.2019.106975
Huang, F.; Zhang, J.; Zhou, C.; Huang J.; Zhu L. (2020). A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides, 17, 217-229. https://doi.org/10.1007/s10346-019-01274-9
Ilia, I.; Tsangaratos, P.; Koumantakis, I.; Rozos, D. (2010). Application of a bayesian approach in GIS based model for evaluating landslide susceptibility. Case study KIMI area, Euboea, Greece. Bulletin of the Geological Society of Greece, 43(3), 1590-1600. https://doi.org/10.12681/bgsg.11333
Ilia, I.; Tsangaratos, P. (2016). Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides, 13(2), 379-397. https://doi.org/10.1007/s10346-015-0576-3
Kadavi, P.R.; Lee, C.W.; Lee, S. (2019). Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environmental Earth Sciences, 78(4). https://doi.org/10.1007/s12665-019-8119-1
Kingma, D.P.; Ba, J.L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations. San Diego, EE.UU.
Lei, T.C.; Wan, S.; Chou, T.Y.; Pai, H.C. (2011). The knowledge expression on debris flow potential analysis through PCA + LDA and rough sets theory: A case study of Chen-Yu-Lan watershed, Nantou, Taiwan. Environmental Earth Sciences, 63(5), 981-997. https://doi.org/10.1007/s12665-010-0775-0
Lin, G.F.; Chang, M.J.; Huang, Y.C.; Ho, J.Y. (2017). Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Engineering Geology, 224, 62-74. https://doi.org/10.1016/j.enggeo.2017.05.009
Lombardo, L.; Mai, P.M. (2018). Presenting logistic regression-based landslide susceptibility results. Engineering Geology, 244, 14-24. https://doi.org/10.1016/j.enggeo.2018.07.019
Luo, W.; Liu, C.C. (2018). Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides, 15(3), 465-474. https://doi.org/10.1007/s10346-017-0893-9
Mahdadi, F.; Boumezbeur, A.; Hadji, R.; Kanungo, D.P.; Zahri, F. (2018). GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria. Arabian Journal of Geosciences, 11(17). https://doi.org/10.1007/s12517-018-3770-5
Oh, H.J.; Kadavi, P.R.; Lee, C.W.; Lee, S. (2018). Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomatics, Natural Hazards and Risk, 9(1), 1053-1070. https://doi.org/10.1080/19475705.2018.1481147
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. https://doi.org/10.1016/j.jseaes.2012.12.014
Pamela; Sadisun, I.A.; Arifianti, Y. (2018). Weights of evidence method for landslide susceptibility mapping in Takengon, Central Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 118(1). https://doi.org/10.1088/1755-1315/118/1/012037
Patriche, C.V.; Pirnau, R.; Grozavu, A.; Rosca, B. (2016). A comparative analysis of binary logistic regression and analytical hierarchy process for landslide susceptibility assessment in the Dobrovăț River Basin, Romania. Pedosphere, 26(3), 335-350. https://doi.org/10.1016/S1002-0160(15)60047-9
Regmi, N.R.; Giardino, J.R.; Vitek, J.D. (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115(1-2), 172-187. https://doi.org/10.1016/j.geomorph.2009.10.002
Sagar, B.S.D.; Cheng, Q.; Agterberg, F. (2018). Handbook of Mathematical Geosciences: Fifty Years of IAMG. Springer International Publishing. https://doi.org/10.1007/978-3-319-78999-6
Servicio Geológico Colombiano. (2015). Zonificación geomecánica y de amenaza por movimientos en masa del municipio de Popayán - Cauca.
Servicio Geológico Colombiano. (2017). Guía Metodológica para la Zonificación de Amenaza por Movimientos en Masa Escala 1:25000.
Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2018.08.003
Trigila, A.; Iadanza, C.; Esposito, C.; Scarascia-Mugnozza, G. (2015). Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology, 249, 119-136. https://doi.org/10.1016/j.geomorph.2015.06.001
van Westen, C.J. (2002). Weights of evidence modeling for landslide susceptibility mapping. International Institute for Geoinformation Science and Earth Observation (ITC), Netherlands.
van Westen, C.J.; Rengers, N.; Soeters, R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30(3), 399-419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e
van Westen, C.J.; Castellanos, E.; Kuriakose, S.L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3-4), 112-131. https://doi.org/10.1016/j.enggeo.2008.03.010
Varnes, D. (1978). Slope movement types and processes. En: R. Schuster; R. Krizek (eds.). Landslides: analysis and control (pp. 11-33). Transportation Research Board.
Wang, Y.; Fang, Z.; Hong, H. (2019). Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Science of the Total Environment, 666, 975-993. https://doi.org/10.1016/j.scitotenv.2019.02.263
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