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References on Artificial Intelligence Models for hydrological application

This page is managed by Hossein Tabari, KU Leuven,


I update, December 2014

II update, April 2015

III update, July 2015

IV update, November 2015

V update, February 2016

VI update, October 2016

References with *star are the last update


Before 2003


ASCE Task Committee on Application of the Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology I: preliminary concepts. Journal of Hydrologic Engineering 5(2): 115–123.
ASCE Task Committee on Application of the Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology II: hydrologic applications. Journal of Hydrologic Engineering 5(2): 124–137.
Coulibaly, P., Anctil, F., Bobe'e, B. (2001) Multivariate reservoir inflow forecasting using temporal neural network. Journal of Hydrologic Engineering 6(5): 367–376.
Dawson, C.W., Wilby, R. (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrological Sciences Journal 43(1): 47–66.
Hsu, K.L., Gupta, H.V., Sorooshian, S. (1995) Artificial neural network modeling of the rainfall-runoff process, Water Resources Research 31(10): 2517–2530.
Maier, H.R., Dandy, G.C. (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modeling and Software 15: 101–23.
Shamseldin, A.Y. (1997) Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology 199: 272–294.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2002) A data driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes 16(6): 1325–1330.
Tokar, A.S., Markus, M. (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. Journal of Hydrologic Engineering 5(2): 156–161.




Cigizoglu, H.K. (2003) Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Science Journal 48(3): 349–361.
Solomatine, D.P., Dulal, K.N. (2003) Model trees as an alternative to neural networks in rainfall—runoff modelling. Hydrological Sciences Journal 48:3 399-411.
Supharatid, S. (2003) Application of a neural network model in establishing a stage–discharge relationship for a tidal river. Hydrological Processes 17: 3085–3099.
Trajkovic, S., Todorovic, B., Stankovic, M. (2003) Forecasting reference evapotranspiration by artificial neural networks. Journal of Irrigation and Drainage Engineering 129(6): 454–457.




Bray, M., Han, D. (2004) Identification of support vector machines for runoff modeling. Journal of Hydroinformatics 6(4): 265–280.
Kisi, O. (2004) River flow modeling using artificial neural network. Journal of Hydrologic Engineering 9(1): 60-63.
Sudheer, K.P, Jain, A. (2004) Explaining the internal behaviour of artificial neural network river flow models. Hydrological Processes 18(4): 833–844.




Chau, K.W., Wu, C.L., Li, Y.S. (2005) Comparison of several flood forecasting models in Yangtze river. Journal of Hydrologic Engineering 10(6): 485–491.
Kumar, A.P.S., Sudheer, K.P., Jain, S.K., Agarwal, P.K. (2005) Rainfall-runoff modeling using artificial neural networks: comparison of network types. Hydrological Processes 19: 1277–1291.
Nayak, P.C., Sudheer, K.P., Rangan, D.M., Ramasastri, K.S. (2005) Short-term flood forecasting with a neurofuzzy model. Water Resources Research 41, W04004, doi:10.1029/2004WR003562.



Chau, K.W. (2006) Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology 329: 363–367.
Dawson, C.W., Abrahart, R.J., Shamseldin, A.Y., Wilby, R.L. (2006) Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology 319: 391–409.
Kişi, Ö. (2006) Generalized regression neural networks for evapotranspiration modeling. Hydrological Science Journal 51: 1092–1105.
Tayfur, G., Singh, V. (2006) ANN and fuzzy logic models for simulating event-based rainfall-runoff. Journal of Hydraulic Engineering 132(12): 1321–1330.

Chang, F.J., Chang, L.C., Wang, Y.S., (2007) Enforced self-organizing map neural networks for river flood forecasting. Hydrological Processes 21(6): 741-749.
Gopakumar, R., Takara, K., James, E.J. (2007) Hydrologic data exploration and river flow forecasting of a humid tropical river basin using artificial neural networks. Water Resources Management (21): 1915-1940.
Kişi, O. (2007) Streamflow Forecasting Using Different Artificial Neural Network Algorithms. Journal of Hydrologic Engineering 12(5): 532-539.
Mishra, A.K., Desai, V.R., Singh, V.P. (2007) Drought forecasting using a hybrid stochastic and neural network model. Journal of Hydrologic Engineering 12(6): 626–638.
Partal, T., Kişi, O. (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology 342: 199–212.

Adamowski, J. (2008) River flow forecasting using wavelet and cross-wavelet transform models. Hydrological Processes 22: 4877–4891.
Jain, S.K., Nayak, P.C., Sudheer, K.P. (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes 22: 2225–2234.
Kim, S., Kim, H.S. (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. Journal of Hydrology 351: 299–317.
Solomatine, D.P, Maskey, M., Shrestha, D.L. (2008) Instance-based learning compared to other data-driven methods in hydrological forecasting. Hydrological Processes 22: 275–287.

Dogan, E. (2009) Reference evapotranspiration estimation using adaptive neuro-fuzzy inference system. Irrigation and Drainage 58: 617–628
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S., Han, D. (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1): 88–97.
Trajkovic, S. (2009) Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration. Hydrological Processes 23: 874–880.
Wang, W., Chau, K., Cheng, C., Qiu, L. (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology 374: 294-306.

Shirsath, P.B., Singh, A.K. (2010) A comparative study of daily pan evaporation estimation using ANN, regression and climate based models. Water Resources Management 24: 1571–1581.
Tabari, H., Marofi, S., Sabziparvar, A.A. (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrigation Sciences 28: 399–406.
Tabari, H., Marofi, S., Zare Abyaneh, H., Sharifi, M.R. (2010) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Computing & Applications 19: 625–635.
Yonaba, H., Anctil, F., Fortin, V. (2010) Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. Journal of Hydrologic Engineering 15(4): 275–283.

Moustris, K.P., Larissi, I.K., Nastos, P.T., Paliatsos, A.G. (2011) Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resources Management 25: 1979-1993.
Saliha, A.H., Awulachew, S.B., Cullmann, J., Horlacher, H.B. (2011) Estimation of flow in ungauged catchments by coupling a hydrological model and neural networks: case study. Hydrology Research 42(5): 386–400.
Yilmaz, A.G., Imteaz, M.A., Jenkins, G. (2011) Catchment flow estimation using Artificial Neural Networks in the mountainous Euphrates Basin. Journal of Hydrology 410: 134–140.

Hosseinzadeh Talaee, P., Heydari, M., Fathi, P., Marofi, S., Tabari, H. (2012) Numerical model and computational intelligence approaches for estimating flow through rockfill dam. Journal of Hydrologic Engineering 17(4): 528–536.
Kisi, O., Moghaddam Nia, A., Ghafari Gosheh, M., Jamalizadeh Tajabadi, M.R., Ahmadi, A. (2012) Intermittent Streamflow Forecasting by Using Several Data Driven Techniques. Water Resources Management 26(2): 457-474.
Rezaeian-Zadeh, M., Tabari, H. (2012) MLP-based drought forecasting in different climatic regions. Theoretical and Applied Climatology 109: 407–414.
Tabari, H., Hosseinzadeh Talaee, P., Abghari, H. (2012) Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron. Meteorology and Atmospheric Physics 116: 147-154.
Tabari, H., Kisi, O., Ezani, A. Hosseinzadeh Talaee, P. (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology 444–445: 78–89.
Zenga, Y., Caib, Y., Jiad, P., Jee, H. (2012) Development of a web-based decision support system for supporting integrated water resources management in Daegu city, South Korea. Expert Systems with Applications 39: 10091–10102.

Abdellatif, M., Atherton, W., Alkhaddar, R. (2013) A hybrid generalised linear and Levenberg–Marquardt artificial neural network approach for downscaling future rainfall in North Western England. Hydrology Research 44(6): 1084–1101.
Badrzadeha, H., Sarukkaligea, R., Jayawardena, A.W. (2013) Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting. Journal of Hydrology, 507: 75–85.
Kim, S., Shiri, J., Kisi, O., Singh, V.P. (2013) Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resources Management 27: 2267–2286.
Kisi, O., Shiri, J., Tombul, M. (2013) Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences 51: 108–117.
Rezaeianzadeh, M, Stein, A., Tabari, H., Abghari, H., Jalalkamali, N., Zia Hosseinipour, E., Singh, V.P. (2013) Comparative assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. International Journal of Environmental Science and Technology 10(16): 1181-1192.
Tabari, H., Hosseinzadeh Talaee, P. (2013) Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Computing & Applications 23(2): 341–348.
Wolfs, V., Willems, P. (2013) A data driven approach using Takagi–Sugeno models for computationally efficient lumped floodplain modelling. Journal of Hydrology 503: 222–232.



Chang, F.J., Chen, P.A., Lu, Y.R., Huang, E., Chang, K.Y. (2014) Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control. Journal of Hydrology 517: 836-846.
Shiri, J., Nazemi, A.H., Sadraddini, A.A., Landeras, G., Kisi, K., Fakheri Fard, A., Marti, P. (2014) Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture 108: 230–241.
Shoaib, M., Shamseldin, A.Y., Melville, B.W. (2014) Comparative study of different wavelet based neural network models for rainfall–runoff modeling. Journal of Hydrology 515: 47-58.
Tsai, M.-J., Abrahart, R.J., Mount, N.J., Chang, F.-J. (2014), Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan. Hydrological Processes 28: 1055–1070.

Wolfs, V., Willems, P. (2014) Development of discharge-stage curves affected by hysteresis using time varying models, model trees and neural networks. Environmental Modelling & Software 55: 107–119.
Wu, J., Long, J., Liu, M. (2014) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148: 136–142.

Buckingham, D., Skalka, C., Bongard, J. (2015) Inductive machine learning for improved estimation of catchment-scale snow water equivalent. Journal of Hydrology 524:311–325.

Chen, X.-Y., Chau, K.W. Wang, W.C., (2015) A Novel Hybrid Neural Network based on Continuity Equation and Fuzzy Pattern-recognition for Downstream Daily River Discharge Forecasting. Journal of Hydroinformatics 17 (5): 733-744.

Cheng, C.T., Niu, W.J., Feng, Z.K., Shen, J.J., Chau, K.W., (2015) Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization. Water 7(8): 4232-4246.

Chen, X.Y., Chau, K.W., Busari, A.O. (2015) A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence 46(A): 258-268.

Chiang, P.-K., Willems, P. (2015) Combine evolutionary optimization with model predictive control in real-time flood control of a river system. Water Resources Management, doi: 10.1007/s11269-015-0955-5.

Fleming, S.W., Bourdin, D.R., Campbell, D., Stull, R.B., Gardner, T. (2015) Development and operational testing of a super-ensemble artificial intelligence flood-forecast model for a Pacific Northwest River. Journal of the American Water Resources Association (JAWRA) 51(2): 502-512.

Gholami, V., Chau, K.W., Fadaee, F., Torkaman, J., Ghaffari, A., “Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. Journal of Hydrology 529 (3): 1060-1069.

Gocic, M., Motamedi, S., Shamshirband, S., Petkovic´, D., Ch, S., Hashim, R., Arif, M. (2015) Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture 113, 164–173.

Malik, A., Kumar, A. (2015) Pan evaporation simulation based on daily meteorological data using soft computing techniques and multiple linear regression. Water Resour Manage (2015) 29:1859–1872.

Olyaie, E., Banejad, H., Chau, K.W. Melesse, A.M., (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environmental Monitoring and Assessment 187(4): 189 22p APR 2015.

Patel, S.S., Ramachandran, P. (2015) A comparison of machine learning techniques for modeling river flow time series: the case of Upper Cauvery River Basin. Water Resources Management 29: 589–602.

Rezaeianzadeh, M., Kalin, L., Anderson, C. (2015) Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis. Journal of Hydrologic Engineering, 10.1061/(ASCE)HE.1943-5584.0001276 , D4015003.

*Tabari, H., Hosseinzadeh Talaee, P. (2015) Reconstruction of river water quality missing data using artificial neural networks. Water Quality Research Journal of Canada, 50(4), 326-335.

Taormina, R., Chau, K., Sivakumar, B. (2015) Neural network river forecasting through baseflow separation and binary-coded swarm optimization. Journal of Hydrology 529(3): 1788-1797.

Wang, W.C., Chau, K.W., Qiu, L., Chen, Y.B. (2015) Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. Environmental Research 139: 46-54.

Youngmin, S., Kim, S., Kisi, O., Singh, V.P. (2015) Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology 520: 224–243.


*Bui, D. T., Pradhan, B., Nampak, H., Bui, Q. T., Tran, Q. A., & Nguyen, Q. P. (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317-330.

*Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., & Kişi, Ö. (2016) Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61(6), 1001-1009.

*Humphrey, G. B., Gibbs, M. S., Dandy, G. C., & Maier, H. R. (2016) A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623-640.

*Shoaib, M., Shamseldin, A. Y., Melville, B. W., & Khan, M. M. (2016) A comparison between wavelet based static and dynamic neural network approaches for runoff prediction. Journal of Hydrology, 535, 211-225.

*Yaseen, Z. M., Kisi, O., Demir, V. (2016) Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence. Water Resources Management, 30(12), 4125-4151.  



Ministero dell'Ambiente e della Tutela del Territorio e del Mare

Institute for Environmental Protection and Research

 Honors Center of Italian Universities - H2CU 
Sapienza University of Rome

University of Tuscia,
Viterbo, Italy


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