Dự đoán sự dịch chuyển khối trung gian của đỉnh đập bằng cách sử dụng mô hình mạng nơ-ron nhân tạo và hồi quy vector hỗ trợ

Agullo L, Aguado A, Mirambell E (1991) A model for the analysis of concrete dams due environmental effects. Int J Numer Methods Heat Fluid Flow 6(4):25–36 Barzegar R, Fijani E, Asghari Moghaddam A, Tziritis E (2017) Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Sci Total Environ 599–600:20–31 Behnia D, Ahangari K, Noorzad A, Moeinossadat SR (2013) Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods. J Zhejiang Univ Sci A (Appl Phys Eng) 14(8):589–602 Bofang Z (2014) Thermal stresses and temperature control of mass concrete. China Institute of Water Resources and Hydropower Research and Chinese Academy of Engineering, Elsevier, Amsterdam Bray M, Han D (2004) Identification of support vector machines for runoff modeling. J Hydroinform 6(4):265–280 Cao M, Qiao P, Ren Q (2009) Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures. Neural Comput Appl 18(7):821–832 Csábrági A, Molnár S, Tanos P, Kovács J (2017) Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube. Ecol Eng 100:63–72 Ebrahimi H, Rajaee T (2017) Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global Planet Change 148:181–191 Elbisy MS (2015) Support vector machine and regression analysis to predict the field hydraulic conductivity of sandy soil. KSCE J Civ Eng 19(7):2307–2316 Emamgholizadeh S, Moslemi K, Karami G (2015) Prediction the groundwater level of Bastam Plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water Resour Manag 28(15):5433–5446 Fedele R, Maier G, Miller B (2006) Health assessment of concrete dams by overall inverse analyses and neural networks. Int J Fract 137(1–4):151–172 Fisher WD, Camp TK, Krzhizhanovskaya VV (2016) Crack detection in earth dam and levee passive seismic data using support vector machines. Proc Comput Sci 80:577–586 French MN, Krajewshi WF, Cuendall PP (1992) Rainfall forecasting in space and time using artificial neural network. J Hydrol 13(7):1–13 Gaziev EG (2000) Safety provision and an expert system for diagnosing and predicting dam behavior. Hydrotech Constr 33(6):285–289 Guang-yong X, Jian-ping Y, Bao-xing Z, Pu T (2011) Application of an artificial immune algorithm on a statistical model of dam displacement. Comput Math Appl 62(10):3980–3986 He Z, Zhang Y, Guo Q, Zhao X (2014) Comparative study of artificial neural networks and wavelet artificial neural networks for groundwater depth data forecasting with various curve fractal dimensions. Water Resour Manag 28(15):5297–5317 Hipni A, El-shafie A, Najah A, Othman A, Aini H, Muhammad M (2013) Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS). Water Resour Manag 27(10):3803–3823 Hosseini SM, Mahjouri N (2016) Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling. Appl Soft Comput 38:329–345 Humphrey GB, Gibbs MS, Dandy GC, Maier HR (2016) A hybrid approach to monthly stream flow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. J Hydrol 540:623–640 Kalteh AM (2015) Wavelet genetic algorithm-support vector regression (wavelet GA–SVR) for monthly flow forecasting. Water Resour Manag 29(4):1283–1293 Kao CY, Loh CH (2013) Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Health Monit 20(3):282–303 Karimi I, Khaji N, Ahmadi MT, Mirzayee M (2010) System identification of concrete gravity dams using artificial neural networks based on a hybrid finite element-boundary element approach. Eng Struct 32(11):3583–3591 Khashei-Siuki A, Sarbazi M (2013) Evaluation of ANFIS, ANN, and geo-statistical models to spatial distribution of groundwater quality (case study: Mashhad plain in Iran). Arab J Geosci 8(2):903–912 Kim S, Shiri J, Kisi O, Singh VP (2013) Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resour Manag 27(7):2267–2286 Labibzadeh M, Khajehdezfuly A (2010) Hydro-thermal safety control of Karun-1 Dam under unusual reservoir level reduction. J Am Sci 6(11):179–184 Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124 Mata J (2011) Interpretation of concrete dam behavior with artificial neural network and multiple linear regression models. Eng Struct 33:903–910 Mirzavand M, Khoshnevisan B, Shamshirband S, Kisi O, Ahmad R, Akib S (2015) Evaluating groundwater level fluctuation by support vector regression and neuro-fuzzy methods: a comparative study. Nat Hazards. https://doi.org/10.1007/s11069-015-1602-4 Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resour Manag 24(9):1845–1865 Mustafa MR, Rezaur RB, Saiedi S, Isa MH (2012) River suspended sediment prediction using various multilayer perceptron neural network training algorithms: a case study in Malaysia. Water Resour Manag 26(7):1879–1897 Popescu TD (2012) Neural network learning for blind source separation with application in dam safety monitoring. Neural Inf Process Lect Notes Comput Sci 7666:1–8 Rankovic V, Nenad A, Dejan DB, Nikola M (2014) Development of support vector regression identification model for prediction of dam structural behavior. Struct Saf 48:33–39 Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition. The PDP Research Group, MIT Press, Cambridge, p 516 Sahoo S, Jha MK (2013) Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeol J 21(8):1865–1887 Salazar F, Toledo MA, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behavior modelling. Struct Saf 56:9–17 Saqib M, Ansari MI (2017) Computation of stresses in concrete gravity dam under seismic loading through ANN and FEM. Proc Eng 173:1779–1783 Seckin N, Cobaner M, Yurtal R, Haktanir T (2013) Comparison of artificial neural network methods with L moments for estimating flood flow at ungauged sites: the case of East Mediterranean River Basin, Turkey. Water Resour Manag 27(7):2103–2124 Senthil Kumar AR, Goyal MK, Ojha CSP, Singh RD, Swamee PK, Nema RK (2013) Application of ANN, fuzzy logic and decision tree algorithms for the development of reservoir operating rules. Water Resour Manag 27(3):911–925 Seo I, Yun SH, Choi SY (2016) Forecasting water quality parameters by ANN model using pre-processing technique at the downstream of Cheongpyeong Dam. Proc Eng 154:1110–1115 Shirzad A, Tabesh M, Farmani R (2014) A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks. KSCE J Civ Eng 18(4):941–948 Stojanovic B, Milivojevic M, Milivojevic N, Antonijevic D (2016) A self-tuning system for dam behavior modeling based on evolving artificial neural networks. Adv Eng Softw 97:85–95 Tayfur G, Nadiri AA, Moghaddam AA (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resour Manag 28(4):1173–1184 Us Army Corps of Engineering (1994) Arch dam design. Engineer Manual 110-2-2201, Chapter 8, pp 1–15 Vapnik VN (1995) The nature of statistical learning theory. Springer, New York, p 187 Vapnik VN (1998) Statistical learning theory. Wiley, New York, p 740 Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396:128–138 Yu P, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328:704–716 Zhou CB, Liu W, Chen YF, Hu R, Wei K (2015) Inverse modeling of leakage through a rockfill dam foundation during its construction stage using transient flow model, neural network and genetic algorithm. Eng Geol 187:183–195