Budayan, C., Dikmen, I., and Birgonul, M. T.: Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping, Expert Syst. Appl., 36, 11772–11781, 2009.
Cao, L.: Support vector machines experts for time series forecasting, Neurocomputing, 51, 321–339, 2003.
Chang, P. C. and Liao, T. W.: Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory, Appl. Soft Comput., 6, 198–206, 2006.
Chen, K. Y. and Wang, C. H.: A hybrid SARIMA and support vector machines in forecasting the production values of machinery industry in Taiwan, Expert System Application, 32, 254–264, 2007.
Chen, S. K., Mangimeli, P., and West, D.: The comparative ability of Self-organizing neural networks to define cluster structure, Omega, International Journal of Management Science, 23, 271–279, 1995.
Cheung, K. H., Szeto, K. Y., and Tam, K. Y.: Maximum-entropy approach to identify time-series lag structure for developing intelligent forecasting systems, International Journal of Computational Intellegence and Organization, 1, 94–106, 1996.
Corzo, G. A., Solomatine, D. P., Hidayat, de Wit, M., Werner, M., Uhlenbrook, S., and Price, R. K.: Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin, Hydrol. Earth Syst. Sci., 13, 1619–1634, https://doi.org/10.5194/hess-13-1619-2009, 2009.
Dibike, Y. B., Velickov, S., Solomatine, D. P., and Abbott, M. B.: Model induction with support vector machines: introduction and applications, ASCE J. Comput. Civil Eng., 15, 208–216, 2001.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology, Hydrol. Earth Syst. Sci., 14, 1931–1941, https://doi.org/10.5194/hess-14-1931-2010, 2010a.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 2: Application, Hydrol. Earth Syst. Sci., 14, 1943–1961, https://doi.org/10.5194/hess-14-1943-2010, 2010b.
Fan, S. and Chen, L.: Short-term load forecasting based on an adaptive hybrid method, IEEE Transactions on Power System, 21, 392–401, 2006.
Fernandez, C. and Vega, J. A.: Streamflow drought time series forecasting: a case study in a small watershed in north west spain, Stoch. Environ. Res. Risk Assess., 23, 1063–1070, 2009.
Firat, M.: Comparison of Artificial Intelligence Techniques for river flow forecasting, Hydrol. Earth Syst. Sci., 12, 123–139, https://doi.org/10.5194/hess-12-123-2008, 2008.
Firat, M. and Gungor, M.: Hydrological time-series modeling using an adaptive neuro-fuzzy inference system, Hydrol. Process., 18, 833–844, 2007.
Gencoglu, M. T. and Uyar, M.: Prediction of flashover voltage of insulators using least square support vector machines, Expert Systems with Applications, 36, 10789–10798, 2009.
Goswami, M., O'Connor, K. M., Bhattarai, K. P., and Shamseldin, A. Y.: Assessing the performance of eight real-time updating models and procedures for the Brosna River, Hydrol. Earth Syst. Sci., 9, 394–411, https://doi.org/10.5194/hess-9-394-2005, 2005.
Hanbay, D.: An expert system based on least square support vector machines for diagnosis of valvular heart disease, Expert Systems with Applictions, 36, 8368–8374, 2009.
Herbst, M. and Casper, M. C.: Towards model evaluation and identification using Self-Organizing Maps, Hydrol. Earth Syst. Sci., 12, 657–667, https://doi.org/10.5194/hess-12-657-2008, 2008.
Herbst, M., Gupta, H. V., and Casper, M. C.: Mapping model behaviour using Self-Organizing Maps, Hydrol. Earth Syst. Sci., 13, 395–409, https://doi.org/10.5194/hess-13-395-2009, 2009.
Hsu, K., Gupta, H. V., Gao, X. Sorooshian, S., and Imam, B.: Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis, Water Resour. Res., 38, 1302, https://doi.org/10.1029/2001WR000795, 2002.
Hsu, S.-H., Hsieh, J. J. P.-A., Chih, T.-C., and Hsu, K.-C.: A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression, Expert Systems with Applications, 36, 7947–7951, 2009.
Huang, C. L. and Tsai, C. Y.: A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting, Expert Syst. Appl., 36, 1529–1539, 2009.
Hung, N. Q., Babel, M. S., Weesakul, S., and Tripathi, N. K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand, Hydrol. Earth Syst. Sci., 13, 1413–1425, https://doi.org/10.5194/hess-13-1413-2009, 2009.
Jain, A. and Kumar, A. M.: Hybrid neural network models for hydrologic time series forecasting, Appl. Soft Comput, 7, 585–592, 2007.
Jacobs, R. A., Jordon, M. A., Nowlan, S. J., and Hinton, G. E.: Adaptive mixtures of local expert, Neural Compt., 3, 79–87, 1991.
Juhos, I., Makra, L., and Toth, B.: Forecasting of traffic orgin NO and NO
2 concentrations by Support Vector Machines and neural networks using principal component analysis, Simulation Modeling Practice and Theory, 16, 1488–1502, 2008.
Kang, S.: An investigation of the Use of Feedforward Neural Network for Forecasting, Kent State University, Ph.D. Thesis, 1991.
Kang, Y. W., Li, J., Cao, G. Y., Tu, H. Y., Li, J., and Yang, J.: Dynamic temperature modeling of an SOFC using least square support vector machines, J. Power Sources, 179, 683–692, 2008.
Khashei, M. and Bijari, M.: An artificial neural network (p,d,q) model for time series forecasting, Expert Syst. Appl., 37, 479–489, 2010.
Kisi, O.: River flow modeling using artificial neural networks, J. Hydrol. Eng., 9, 60–63, 2004.
Kisi, O.: River flow forecasting and estimation using different artificial neural network technique, Hydrol. Res., 39.1, 27–40, 2008.
Kisi, O. and Cimen, M.: A wavelet-support vector machine conjunction model for monthly streamflow forecasting, J. Hydrol., 399, 132–140, 2010.
Kohonen, T.: Self-Organizing Maps, New York,Springer, 501, 2001.
Lin, G. F. and Chen, L. H.: Time series forecasting by combining the radial basis function network and the Self-Organizing Map, Hydrol. Process., 19, 1925–1937, 2005.
Lin, G. F. and Chen, L. H.: Identification of homogeneous regions for regional frequency analysis using the Self-Organizing Map, J. Hydrol., 324, 1–9, 2006.
Lin, G. F. and Wu, M. C.: A SOM-based approach to estimating design hyetographs of ungauged sites, J. Hydrol., 339, 216–226, 2007.
Lin, G. F. and Wu, M. C.: A hybrid neural network model for typhoon-rainfall forecasting, J. Hydrol., 375, 216–226, 2009.
Liu, L. and Wang, W.: Exchange rates forecasting with least squares support vector machines, International Conference on Computer Science and Software Engineering, 1017–1019, 2008.
Luchetta, A. and Manetti, S.: A real time hydrological forecasting system using a fuzzy clustering approach, Comput. Geosci., 29, 1111–1117, 2003.
Maier, H. R. and Dandy, G. C.: Neural networks for the production and forecasting of water resource variables: a review and modelling issues and application, Environ. Model. Softw., 15, 101–124, 2000.
Modarres, R.: Streamflow drought time series forecasting, Stoch. Environ. Res. Risk Assess., 21, 223–233, 2007.
Murao, H., Nishikawa, I., Kitamura, S., Yamada, M., and Xie, P.: A hybrid neural network system for the rainfall estiamtion using saellite imagery. In: Proceedings of International Joint Conference on Neural Networks, IEEE press, 1211–1214, 1993.
Pai, P. F. and Lin, C. S.: A hybrid ARIMA and support vector machines model in stock price forecasting, Omega, 33, 497–505, 2005.
Pal, N. R., Pal, S., Das, J., and Majumdar, K,: SOFM-MLP: A hybrid neural network for atmospheric temperature prediction, IEEE T. Geosci. Remote Sens., 41, 2783–2791, 2003.
Partal, T. and Kisi, O.: Wavelet and neuro-fuzzy conjuction model for precipitation forecasting, J. Hydrol., 342, 199–212, 2007.
Srikanthan, R. and McMahon, T. A.: Stochastic generation of annual, monthly and daily climate data: A review, Hydrol. Earth Syst. Sci., 5, 653–670, https://doi.org/10.5194/hess-5-653-2001, 2001.
Srinivas, V. V., Tripathi, S., Rao, A. R., and Govindaraju, R. S.: Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering, J. Hydrol., 348, 148–166, 2009.
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle, J.: Least squares support vector machines, World Scientific, Singapore, 2002.
Tay, F. E. H. and Cao, L. J.: Improved financial time series forecasting by combining support vector machine with self- organizing feature map, Intell. Data Anal., 5, 339–354, 2001.
Vapnik, V.: The nature of Statistical Learning Theory, Springer Verlag, Berlin, 1995.
Wang, J. and Yan, D.: A high precision prediction method by using combination of ELMAN and SOM neural networks. In Proceeding of the first international symposium on neural networks China, 943–949, 2004.
Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L.: A Comparison of Performance of Several Artificial Intelligence Methods for Forecasting Monthly Discharge Time Series, J. Hydrol., 374, 294–306, 2009.
Wang, W., Pieter H. A. J. M. V. G., Vrijling, J. K., and Ma, J.: Forecasting daily streamflow using hybrid ANN models, J. Hydrol., 324, 383–399, 2006.
Wong, F. S.: Time series forecasting using backpropagation neural networks, Neurocomputing, 2, 147–159, 1991.
Wu, C. L. and Chau, K. W.: A hybrid model coupled with singular spectrum analysis for daily rainfall prediction, J. Hydroinform., 12.4, 458–473, 2010.
Wu, C. L., Chau, K. W., and Li, Y. S.: Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques, Water Resour. Res., 45, W08432, https://doi.org/10.1029/2007wr006737, 2009.
Wu, S. and Chow, T. W. S.: Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density, Pattern Recognition, 37, 175–188, 2004.
Yue, S. and Wang, C.: The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series, Water Resour. Manage., 18 , 201–218, 2004.
Yunrong, X. and Liangzhong, J.: Water quality prediction using LS-SVM with particle swarm optimization, Second International Workshop on Knowledge Discovery and Data Mining, 900–904, 2009.
Zhang, B. and Govindaraju, G.: Prediction of watershed runoff using Bayesian concepts and modular neural network, Water Research Res., 36, 752–762, 2000.
Zhang, G. P.: Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, 159–175, 2003.
Zou, H. F., Xia, G. P., Yang, F. T., and Wang, H. Y.: An investigation and comparison of artificial neural network and time series models for chinese food grain price forecasting, Neurocomputing, 70, 2913–2923, 2007.
