PSI - Issue 17

Ahmed Belmokre et al. / Procedia Structural Integrity 17 (2019) 698–703 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Both models are a good alternative to classical statistical models or deterministic models. Moreover, its nature allows then to handle nonlinear relationships between variables without the need of establish any hypothesis regarding the shape of the relationship. Acknowledgements The work goes into the preparation of doctoral thesis at LaboratoryWater Resource Mobilization and Enhancement (MVRE), National High School for Hydraulic (ENSH). References Basak, D., Pal, S. & Chandra Patranabis, 2007. Support vector regression. Neural Information Processing – Letters and Reviews 11, 203-224. Breiman, L. 2001. Random Forests. Machine Learning 45,5-32. Drucker, H., Burges, C.J.C, Linda Kaufman, Smola, A.J & Vapnik, V., 1995. Support vector regression machines. Advances in neural information processing systems, 155-161. Grömping, U., 2009. Variable Importance Assessment in Regression: Linear Regression versus Random Forest. The American Statistician 63, 308 319. Genuer, R., Poggi J.M. & Malot, C.T., 2010. Variable selection using random forests. Pattern recognition letters 31, 2225-2236. Junrui, C. 2002, Analysis of coupled seepage and temperature fields in concrete dam. Numerical methods in engineering 18, 399-409. Li, P., Lu, W., Long, Y., Yang, Z. & Li, J., 2008. Seepage analysis in a fractured rock mass: the upper reservoir of Pushihe pumped-storage power station in China. Engineering Geology 97,53 – 62. Li, M., Guo, X., Shi, J. & Zhu, Z., 2015. Seepage and stress analysis of anti-seepage structures constructed with different concrete materials in an RCC gravity dam. Water Science and Engineering 8, 326-334. Philip, J.R. 1957. Evaporation, and moisture and heat fields in the soil. Journal of Meteorology 14, 354 – 366. Rubertis, K.D. 2018. Monitoring Dam Performance: Instrumentation and Measurements. Virginia. American society of civil engineers. Santillan, D., Ardanuy, J.F., Morán, R. & Toledo, M.A., 2011. Forecasting of dam flow-meter measurements using Artificial Neural Networks. In Romeo García et al. (eds), Taylor & Francis Group; Proc. Dam Maintenance and Rehabilitation II, Zaragoza, Spain, 23-25 November 2010. London. Santillán, D., Fraile-Ardanuy, J., and Toledo, M. Á., 2014. Predicción de lecturas de aforos de filtraciones de presas bóveda mediante redes neuronales artificiales. Tecnol Cienc. Agua 5, 81-96. Salazar, F., Morán, R., Toledo, M.Á., & Oñate, E., 2015. Data-based models for the prediction of dam behavior: A review and some methodological considerations. Archives of Computational Methods in Engineering 1. Vapnik, V., 1995. The Nature of Statistical Learning Theory, Springer. Wu, Y., Zhang, Z., 1995. Introduction to Rock Mass Hydraulics. Southwest Jiaotong University Press: Chengdu. China.