Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification

Journal of Process Control - Tập 86 - Trang 44-56 - 2020
Baya Hadid1, Éric Duviella1, S. Lecœuche1
1IMT Lille Douai, University of Lille, Informatics and Automatics Research Unit, Lille F-59000, France

Tóm tắt

Từ khóa


Tài liệu tham khảo

Perrin, 2001, Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments, J. Hydrol., 242, 275, 10.1016/S0022-1694(00)00393-0

Elshorbagy, 2010, Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - part 2: application, Hydrol. Earth Syst. Sci., 14, 1943, 10.5194/hess-14-1943-2010

Asefa, 2006, Multi-time scale stream flow predictions: the support vector machines approach, J. Hydrol., 318, 7, 10.1016/j.jhydrol.2005.06.001

Siou, 2010, Flash floods forecasting in a karstic basin using neural networks: the case of the lez basin (south of france)

Nayak, 2004, A neuro-fuzzy computing technique for modeling hydrological time series, J. Hydrol., 291, 52, 10.1016/j.jhydrol.2003.12.010

Badrzadeh, 2017, Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model, Hydrol. Res., 49, 27, 10.2166/nh.2017.163

Dariane, 2017, Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection, J. Hydroinformatics, 20, 520, 10.2166/hydro.2017.076

Chang, 2018, Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure, J. Hydrol., 564, 1179, 10.1016/j.jhydrol.2018.07.074

Young, 1986, Time series methods and recursive estimation in hydrological systems analysis, 129

Tóth, 2007, Discrete time lpv i/o and state space representations, differences of behavior and pitfalls of interpolation, Proc. of the European Control Conf., Kos, Greece, 5418

Previdi, 2009, Identification of parametrically-varying models for the rainfall-runoff relationship in urban drainage networks, 42, 1768

Laurain, 2010, Identification de modèles LPV : application à la modélisation pluie/débit d’un bassin versant viticole

Duviella, 2012, Predictive Black-Box Modeling Approaches for Flow Forecasting of the Liane River.

Ljung, 1999

Nash, 1970, River flow forecasting through conceptual models part i: a discussion of principles, J. Hydrol., 10, 282, 10.1016/0022-1694(70)90255-6

Perrin, 2003, Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275, 10.1016/S0022-1694(03)00225-7

Edijatno, 1989, Un modèle pluie-débit journalier à trois paramètres, La Houille Blanche, 2, 113, 10.1051/lhb/1989007

Edijatno, 1999, GR3J: a daily watershed model with three free parameters, Hydrol. Sci. J., 44, 263, 10.1080/02626669909492221

Bourgin, 2014, Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting, J. Hydrol., 519, 2775, 10.1016/j.jhydrol.2014.07.054

Ficchi, 2017

Dakhlaoui, 2017, Evaluating the robustness of conceptual rainfall-runoff models under climate variability in northern tunisia, J. Hydrol., 550, 201, 10.1016/j.jhydrol.2017.04.032

Bastin, 2009, Online river flow forecasting with hydromax : successes and challenges after twelve years of experience

Sjoberg, 1995, Nonlinear black-box modeling in system identification: a unified overview, Automatica, 31, 1691, 10.1016/0005-1098(95)00120-8

Paoletti, 2007, Identification of hybrid systems a tutorial, Eur. J. Control, 13, 242, 10.3166/ejc.13.242-260

Vidal, 2003, An algebraic geometric approach to the identification of a class of linear hybrid systems, 1, 167

Vidal, 2004, Identification of PWARX hybrid models with unknown and possibly different orders, 1, 547

Juloski, 2005, A bayesian approach to identification of hybrid systems, IEEE Trans. Autom. Control, 50, 1520, 10.1109/TAC.2005.856649

Bemporad, 2005, A bounded-error approach to piecewise affine system identification, IEEE Trans. Autom. Control, 50, 1567, 10.1109/TAC.2005.856667

Ferrari-Trecate, 2003, A clustering technique for the identification of piecewise affine systems, Automatica, 39(2), 205, 10.1016/S0005-1098(02)00224-8

Lauer, 2014, Piecewise smooth system identification in reproducing kernel hilbert space, 6498

Boukharouba, 2009, Identification of piecewise affine systems based on Dempster-Shafer theory, 1662

Lauer, 2011, A continuous optimization framework for hybrid system identification, Automatica, 47, 608, 10.1016/j.automatica.2011.01.020

Bako, 2014, Subspace clustering through parametric representation and sparse optimization, IEEE Signal Process. Lett., 21, 356, 10.1109/LSP.2014.2303122

Kersting, 2017, Recursive estimation in piecewise affine systems using parameter identifiers and concurrent learning, Int. J. Control, 0, 1

Bako, 2011, A recursive identification algorithm for switched linear/affine models, Nonlinear Anal., 5, 242

Breschi, 2016, Identification of hybrid and linear parameter varying models via recursive piecewise affine regression and discrimination, 2632

Breschi, 2016, Piecewise affine regression via recursive multiple least squares and multicategory discrimination, Automatica, 73, 155, 10.1016/j.automatica.2016.07.016

Shafer, 1976

Denoeux, 1995, A k-nearest neighbor classification rule based on dempster-shafer theory, IEEE Trans. Syst. Man. Cybern., 25, 804, 10.1109/21.376493

Smets, 1994, The transferable belief model, Artif. Intell., 66, 191, 10.1016/0004-3702(94)90026-4

Hadid, 2017, Data assignment and parameter adaptation for switched LPV system estimation, 4564

Vapnik, 1995

Bredensteiner, 1999, Multicategory classification by support vector machines, Comput. Optim. Appl., 12, 53, 10.1023/A:1008663629662

Ohlsson, 2013, Identification of switched linear regression models using sum-of-norms regularization, Automatica, 49, 1045, 10.1016/j.automatica.2013.01.031

http://www.hydro.eaufrance.fr, (2019).

Norbiato, 2008, Flash flood warning based on rainfall thresholds and soil moisture conditions: an assessment for gauged and ungauged basins, J. Hydrol., 362, 274, 10.1016/j.jhydrol.2008.08.023

Kong, 2015, Wind speed prediction using reduced support vector machines with feature selection, Neurocomputing, 169, 449, 10.1016/j.neucom.2014.09.090

Hay, 1988, The derivation of global estimates from a confusion matrix, Int. J. Remote Sens., 9, 1395, 10.1080/01431168808954945

Sammut, 2011

Young, 2003, Top-down and data-based mechanistic modelling of rainfall-flow dynamics at the catchment scale, Hydrol. Processes, 17, 2195, 10.1002/hyp.1328

Levenberg, 1944, A method for the solution of certain non-linear problems in least squares, Q. Appl. Math., 2, 164, 10.1090/qam/10666

Marquardt, 1963, An algorithm for least-squares estimation of nonlinear parameters, J. Soc. Ind. Appl. Math., 11, 431, 10.1137/0111030

Young, 1979, Refined instrumental variable methods of recursive time-series analysis part i. single input, single output systems, Int. J. Control, 29, 1, 10.1080/00207177908922676