Artículos científicos
Doble evaluación de la susceptibilidad por movimientos en masa basada en redes neuronales artificiales y pesos de evidencia
Paul Goyes-Peñafiel- Alejandra Hernandez-Rojas
Publicado 2021-01-07
Palabras clave
- Susceptibilidad por movimientos en masa,
- Aprendizaje Profundo,
- Regresión Logística,
- Pesos de Evidencia,
- Análisis de Componentes Principales
- Redes Neuronales Artificiales ...Más
Cómo citar
Goyes-Peñafiel, P., & Hernandez-Rojas, A. (2021). Doble evaluación de la susceptibilidad por movimientos en masa basada en redes neuronales artificiales y pesos de evidencia. Boletín De Geología, 43(1), 173–191. https://doi.org/10.18273/revbol.v43n1-2021009
Derechos de autor 2021 Boletín de Geología
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Altmetrics
Resumen
Los movimientos en masa son la amenaza natural más frecuente en zonas tropicales y son causantes de graves daños en la infraestructura vial, pérdida de vidas humanas y consecuencias económicas. Por lo tanto, una evaluación cuantitativa y confiable de la susceptibilidad por movimientos en masa es esencial para el desarrollo y planeación territorial. En este trabajo se estudia el cálculo de la susceptibilidad con un bajo nivel de incertidumbre a partir de la integración metodológica de Pesos de Evidencia con las Redes Neuronales Artificiales. El primero se usó para extraer los valores de los pesos de las variables y el inventario de movimientos en masa, y el segundo para establecer una relación no lineal entre los factores condicionantes y el inventario puntual de movimientos en masa realizado a partir del estudio geológico y geomorfológico del municipio de Popayán. Esto genera una doble verificación que permite extraer las características de las variables categóricas y continuas para generar relaciones más precisas de susceptibilidad, evitando la multicolinealidad y el uso de factores no significantes a partir del Análisis de Componentes Principales. Para estudiar la influencia de las variables, se analizaron dos propuestas metodológicas, la primera con dos variables y la segunda con cinco variables explicativas. Para cada una de ellas se aplicaron los métodos cuantitativos de Regresión Logística, Perceptrón Multicapa y Red Neuronal Profunda como elementos de doble verificación. Los resultados fueron evaluados a partir de la curva Característica Operativa del Receptor para cada modelo, encontrando que las redes neuronales profundas tienen un valor de Área Bajo la Curva de 0,902 y 0,969 para las propuestas 1 y 2 respectivamente, superando al método de Pesos de Evidencia usado convencionalmente y a la Regresión Logística como métodos cuantitativos.
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Referencias
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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
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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
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