Application of Several Data-Driven Techniques for Rainfall-Runoff Modeling

Authors
M. Vafakhah 1
S. Janizadeh 2
S. Khosrobeigi Bozchaloei 3
1 Department of Watershed Management Engineering, College of Natural Resources & Marine Sciences, Tarbiat Modares University, I. R. Ira
2 M.Sc. Student, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran.
3 Former M.Sc. Student, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Iran.
Abstract
In this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and wavelet-artificial neural network (Wavelet-ANN) models were applied to model rainfall-runoff (RR) relationship. For this purpose, the daily stream flow time series of hydrometric station of Hajighoshan on Gorgan River and the daily rainfall time series belonging to five meteorological stations (Houtan, Maravehtapeh, Tamar, Cheshmehkhan and Tangrah climatologic stations) were used for period of 1983-2007. Root mean square error (RMSE) and correlation coefficient (r) statistics were employed to evaluate the performance of the ANN, ANFIS, ARX and ARMAX models for rainfall-runoff modeling. The results showed that ANFIS models outperformed the system identification, ANN and Wavelet-ANN models. ANFIS model in which preprocessed data using fuzzy interface system was used as input for ANN which could cope with non-linear nature of time series and performed better than others.
Keywords
ANFIS
ANN
system identification
Wavelet-ANN
Rainfall-runoff modeling
Adamowski, J. and Chan, H.F. A wavelet neural network conjunction model for groundwater level forecasting. J. Hydrol., 2011; 407 (1): 28-40.
Anctil, F., Perrin, C. and Andreassian, V. ANN output updating of lumped conceptual rainfall/runoff forecasting models1. JAWRA J. Am. Water Resur. Assoc., 2003; 39 (5): 1269-1279.
Anctil, F. and Tape, D.G. An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition. J. Environ. Eng. Sci., 2004; 3 (S1): S121-S128.
Anmala, J., Zhang, B. and Govindaraju, R.S. Comparison of ANNs and empirical approaches for predicting watershed runoff. J. Water Res. Pl. Manage., 2000; 126 (3): 156-166.
Aqil, M., Kita, I., Yano, A. and Nishiyama, S. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J. Hydrol., 2007; 337 (1): 22-34.
Asadi, S., Shahrabi, J., Abbaszadeh, P. and Tabanmehr, S. A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing., 2013; 121: 470-480.
Aussem, A., Murtagh, F. and Clermont-ferr, B.P. A neuro-wavelet strategy for web traffic forecasting. J. Official Statistics, 1998; 1:65-87.
Baratti, R., Cannas, B., Fanni, A., Pintus, M., Sechi, G.M. and Toreno, N. River flow forecast for reservoir management through neural networks. Neurocomputing., 2003; 55 (3): 421-437.
Bárdossy, A. and Disse, M. Fuzzy rule - based models for infiltration. Water Resour. Res., 1993; 29(2): 373-382.
Bhattacharya, B. and Solomatine, D.P. Machine learning in sedimentation modelling. Neural Networks, 2006; 19 (2): 208-214.
Bogardi, I., Bardossy, A., Duckstein, L. and Pongracz, R. Fuzzy logic in hydrology and water resources. Fuzzy Logic in Geology, (eds: Demicco, R.V. and Klir, G.J.), Elsevier, Academic Press, 2003; 153-190.
Bruen, M. and Yang, J. Functional networks in real-time flood forecasting-a novel application. Adv. Water Resour., 2005; 28 (9): 899-909.
Campolo, M., Andreussi, P. and Soldati, A. River flood forecasting with a neural network model. Water Resour. Res., 1999; 35 (4): 1191-1197.
Campolo, M., Soldati, A. and Andreussi, P. Artificial neural network approach to flood forecasting in the River Arno. Hydrol. Sci. J., 2003; 48 (3): 381-398.
Cannas, B., Fanni, A., See, L. and Sias, G. Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys. Chem. Earth., Parts A/B/C, 2006; 31(18): 1164-1171.
Castellano-Méndez, M.a., González-Manteiga, W., Febrero-Bande, M., Manuel Prada-Sánchez, J., Lozano-Calderón, R. Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box–Jenkins and neural networks methods. J. Hydrol., 2004; 296 (1): 38-58.
Celik, O. and Ertugrul, S. Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques. Eng. Appl. Artif. Intell., 2010; 23 (4): 595-603.
Cigizoglu, H.K. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv. Water Resour., 2004; 27 (2): 185-195.
Coulibaly, P., Anctil, F. and Bobée, B. Prévision hydrologique par réseaux de neurones artificiels: état de l'art. Can. J. Civil Eng., 1999; 26 (3): 293-304.
Daliakopoulos, I.N., Coulibaly, P. and Tsanis, I.K., Groundwater level forecasting using artificial neural networks. J. Hydrol., 2005; 309 (1): 229-240.
Dibike, Y.B. and Solomatine, D.P. River flow forecasting using artificial neural networks. Phys. and Chem.Earth, PT B, 2001; 26 (1): 1-7.
Dorum, A., Yarar, A., Faik Sevimli, M. and Onüçyildiz, M. Modelling the rainfall-runoff data of susurluk basin. Expert Syst. Appl., 2010; 37 (9): 6587-6593.
Erdoğan, H. and Gülal, E. Identification of dynamic systems using multiple input–single output (MISO) models. Nonlinear Analysis: Real World Appl., 2009; 10 (2): 1183-1196.
Hagan, M.T. and Menhaj, M.B. Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Netw., 1994; 5 (6): 989-993.
Haykin, S. Blind Deconvolution (Prentice Hall Information and System Sciences), Prentice Hall. 1994, 288p.
Hsu, K.-l., Gupta, H.V. and Sorooshian, S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res., 1995; 31 (10): 2517-2530.
Jain, A. and Srinivasulu, S. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. J. Hydrol., 2006; 317 (3): 291-306.
Jang, J.-S. ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst., Man Cybern., 1993; 23 (3): 665-685.
Khan, M.S. and Coulibaly, P. Bayesian neural network for rainfall-runoff modeling. Water Resour. Res., 2006; 42 (7).
Kisi, E.H. and Elcombe, M.M. U parameters for the wurtzite structure of ZnS and ZnO using powder neutron diffraction. Acta Crystallogr. Sect. C-Cryst. Struct. Commun., 1989; 45 (12): 1867-1870.
Kisi, O., Shiri, J. and Tombul M. Modeling rainfall-runoff process using soft computing techniques. Comput. Geosci., 2013; 51: 108-117.
Ljung, L. MATLAB: System Identification Toolbox: User's Guide Version 4. The Mathworks, 1995. 408p.
Lohani, A., Goel, N. and Bhatia, K. Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship. J. Hydrol., 2006; 331(1): 146-160.
Mallat, S.G. A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell., 1989; 11(7): 674-693.
Melching, C.S., Yen, B.C. and Wenzel Jr, H.G. Output reliability as guide for selection of rainfall-runoff models. J. Water Resour. Pl. Manage., 1991; 117(3): 383-398.
Mohammadi, K. Groundwater table estimation using MODFLOW and artificial neural networks, Pract. Hydroinform., 2008; 127-138.
Moosavi, V. and Vafakhah, M., Shirmohammadi, B., Behnia, N. A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods. Water Resour. Manage., 2013; 1-21.
Nason, G.P. and Von Sachs, R. Wavelets in time-series analysis. Philos. Trans. Roy. Soc. London. Ser. A Mat. Phys. Eng. Sci., 1999; 357(1760): 2511-2526.
Nayak, P., Sudheer, K. and Rangan, D., Ramasastri, K. A neuro-fuzzy computing technique for modeling hydrological time series. J. Hydrol., 2004; 291(1): 52-66.
Nayak, P., Venkatesh, B., Krishna, B. and Jain, S.K., 2013. Rainfall runoff modelling using conceptual, data driven and wavelet based computing approach. J. Hydrol., 2013; 493:57-67.
Partal, T., Cigizoglu, H.K. Estimation and forecasting of daily suspended sediment data using wavelet–neural networks. J. Hydrol., 2008; 358 (3): 317-331.
Rajurkar, M., Kothyari, U. and Chaube, U. Modeling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol., 2004; 285 (1): 96-113.
Sajikumar, N. and Thandaveswara, B. A non-linear rainfall–runoff model using an artificial neural network. J. Hydrol., 1999; 216(1): 32-55.
Shamseldin, A.Y. Application of a neural network technique to rainfall-runoff modelling. J. Hydrol., 1997; 199 (3): 272-294.
Shiri, J. and Kisi, O. Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J. Hydrol., 2010; 394 (3-4): 486-493.
Shirmohammadi, B., Vafakhah, M., Moosavi, V. and Moghaddamnia, A. Application of several data-driven techniques for predicting groundwater level. Water Resour. Manage., 2013; 27 (2): 419-432.
Talei, A., Chua, L.H.C. and Quek, C. A novel application of a neuro-fuzzy computational technique in event-based rainfall–runoff modeling. Expert Syst. Appl., 2010; 37 (12): 7456-7468.
Talei, A., Chua, L.H.C. and Wong, T.S.W. Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J. Hydrol., 2010; 391 (3-4): 248-262.
Tokar, A.S. and Johnson, P.A. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng., 1999; 4 (3): 232-239.
Tokar, A.S. and Markus, M. Precipitation-runoff modeling using artificial neural networks and conceptual models. J. Hydrol. Eng., 2000; 5 (2): 156-161.
Vafakhah, M. Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Canadian J. Civil. Eng., 2012; 39 (4): 402-414.
Vafakhah, M. Comparison of cokriging and adaptive neuro-fuzzy inference system models for suspended sediment load forecasting. Arab. J. Geos., 2013; 1-16.
Wang, W. and Ding, J. Wavelet network model and its application to the prediction of hydrology. Nat. Sci., 2003; 1 (1): 67-71.
Xiong, L., O Connor, K.M. and Goswami, M. Application of the artificial neural network (ANN) in flood forecasting on a karstic catchment, Proceedings Of The Congress-International Assoc. Hydraulic Res., 2001; 29-35.
Yurekli, K., Kurunc, A. and Simsek, H. Prediction of daily maximum streamflow based on stochastic approaches. J. Spatial Hydrol., 2012; 4(2).
Zhou, H., Wu, S., Joo, J.Y., Zhu, S., Han, D.W., Lin, T., Trauger, S., Bien, G., Yao, S., Zhu, Y., Siuzdak, G., Schöler, H.R., Duan, L. and Ding, S. Generation of induced pluripotent stem cells using recombinant proteins. Cell Stem Cell, 2009; 4(5): 381-384.
ECOPERSIA
Volume 2, Issue 1 - Serial Number 7
March 2014
Pages 455-469