امیری، ا.، و رودباری موسوی، م.م. (1395). ارزیابی مدل هیدرولوژی IHACRES در شبیه‌سازی دبی روزانه (مطالعۀ موردی رودخانه‌های پل‌رود و شلمان‌رود). اکوهیدرولوژی، 3(4)، 533-543.
زارع زاده مهریزی، ش.، خورانی، ا.، بذرافشان، ج.، و بذرافشان، ا. (1396). ارزیابی کارآیی مدل SWAT در شبیه سازی رواناب حوضه آبریز گاماسیاب. مرتع و آبخیزداری، مجلۀ منابع طبیعی ایران، 70(4)، 881-893.
زین الدینی، س.، انوری، ص.، و زحمتکش، ز. (1397). کاربرد رویکردهای شبیه سازی-بهینه سازی جهت ارزیابی گزینه‌های مختلف اقلیمی و مدیریتی در یک سامانۀ منابع آب. تحقیقات منابع آب ایران، 14(5)، 295-310.
Adamowski, J., & Chan, H.F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407, 28-40. doi:10.1016/j.jhydrol.2011.06.013.
Ahmadi, M., Moeini, A., Ahmadi, H., Motamedvaziri, B., & Zehtabiyan, G.R. (2019). Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran). Physics and Chemistry of the Earth, 111, 65-77. doi:10.1016/j.pce.2019.05.002.
Ahooghalandari, M., Khiadani, M., & Kothapalli, G., (2016). Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia. Australian Journal of Water Resources, 19, 116-126. doi:10.1080/13241583.2015.1116183.
Amiri, E., Roudbari Mousavi, M.M. (2016). Evaluation of IHACRES hydrological model for simulation of daily flow (case study Polrood and Shalmanrood rivers). Iranian Journal of Eco Hydrology, 3(4), 533-543 (in Persian).
Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., Santhi, C., Harmel, R.D., Van Griensven, A., Van Liew, M.W., Kannan, N., & Jha, M.K. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55, 1491-1508.
ASCE, (2000). Artificial Neural Networks in Hydrology II: Hydrologic Applications. Journal of Hydrologic Engineering, 5, 124-137. doi:10840699/00/0002-01240137.
Beck, M.B. (1991). Forecasting environmental change. Journal of Forecasting, 10, 3-19. doi:10.1002/for.3980100103.
Carcano, E.C., Bartolini, P., Muselli, M., & Piroddi, L. (2008). Jordan recurrent neural network versus IHACRES in modelling daily streamflows. Journal of Hydrology, 362, 291-307. doi:10.1016/j.jhydrol.2008.08.026.
Chen, L.H., Chen, C.T., & Pan, Y.G. (2010). Groundwater Level Prediction Using SOM-RBFN Multisite Model. Journal of Hydrologic Engineering, 15, 624-631. doi:10.1061/(asce)he.1943-5584.0000218.
Coulibaly, P., Anctil, F., & Bobee, B. (2000). Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology, 230, 244-257. doi:10.1016/S0022-1694(00)00214-6.
Croke, B.F.W., & Jakeman, A.J. (2007). Use of the IHACRES rainfall-runoff model in arid and semi-arid regions. Hydrological Modelling in Arid and Semi-Arid Areas, 41–48. doi:10.1017/CBO9780511535734.005.
Croke, B.F.W., Andrews, F., Jakeman, A.J., Cuddy, S.M., & Luddy, A. (2006). IHACRES Classic Plus: A redesign of the IHACRES rainfall-runoff model. Environmental Modelling & Software, 21, 426 - 427.
Ebrahimi, H., & Rajaee, T. (2017). Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global and Planetary Change, 148, 181-191. doi:10.1016/j.gloplacha.2016.11.014.
Gong, Y., Zhang, Y., Lan, S., & Wang, H. (2016). A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida. Water Resources Management, 30, 375-391. doi:10.1007/s11269-015-1167-8.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.
Hsu, K.l., Gupta, H.V., Adamowski, J., Fung Chan, H., Prasher, S.O., Ozga-Zielinski, B., & Sliusarieva, A. (1995). Artificial neural network modeling of the rainfall-runoff process that arise and based Background and Scope. Water Resources Research, 48, 1-14.
Jakeman, A., Littlewood, I., & Whitehead, P. (1993). An assessment of the dynamic response characteristics of streamflow in the Balquhidder catchments. Journal of Hydrology, 145(3), 337-355.
Jha, M.K., & Sahoo, S. (2015). Efficacy of neural network and genetic algorithm techniques in simulating spatiotemporal fluctuations of groundwater. Hydrological Processes, 29, 671-691. doi:10.1002/hyp.10166.
Jimeno-Saez, P., Senent-Aparicio, J., Perez-Sanchez, J., & Pulido-Velazquez, D. (2018). A comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of peninsular Spain. Water (Switzerland), 10. doi:10.3390/w10020192.
Kagoda, P.A., Ndiritu, J., Ntuli, C., & Mwaka, B. (2010). Application of radial basis function neural networks to short-term streamflow forecasting. Physics and Chemistry of the Earth, 35, 571-581. doi:10.1016/j.pce.2010.07.021.
Kang, K.W., Kim, J.H., Park, C.Y., & Ham, K. J. (1993). Evaluation of hydrological forecasting system based on neural network model. In: Proceedings of the 25th Congress of the International Association for Hydraulic Research, Delft, Netherlands, Pp. 257-264.
Kegl, B., Krzyak, A., & Niemann, H. (2000). Radial basis function networks and complexity regularization in function learning and classification. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, IEEE Comput. Soc, Barcelona, Spain, Pp. 81-86. doi:10.1109/ICPR.2000.906022.
Kim, J.W., & Pachepsky, Y.A. (2010). Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology, 394, 305-314, doi:10.1016/j.jhydrol.2010.09.005.
Kim, H., & Lee, S. (2014). Assessment of a seasonal calibration technique using multiple objectives in rainfall–runoff analysis. Hydrological Processes, 28(4), 2159-2173.
Kim, K.B. (2015). Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD. Journal of Hydrology, 521, 543-555. doi:10.1016/j.jhydrol.2014.12.030.
Kim, K.B., Kwon, H-H., & Han, D. (2018). Exploration of warm-up period in conceptual hydrological modelling. Journal of Hydrology, 556, 194-210, doi:10.1016/j.jhydrol.2017.11.015.
Kim, T.W., & Valdes, J.B. (2003). Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks. Journal of Hydrologic Engineering, 8, 319-328, doi:10.1061/(asce)1084-0699(2003)8:6(319).
Kumar, D.N., Raju, K.S., & Sathish, T., (2004). River flow forecasting using artificial neural networks. Water Resources Management, 2, 143-161, doi:10.1016/j.asoc.2019.04.024.
Letcher, R., Schreider, S.Y., Jakeman, A., Neal, B., & Nathan, R. (2001). Methods for the analysis of trends in streamflow response due to changes in catchment condition. Environmetrics, 12(7), 613-630, doi:10.1002/env.486
Meng, X., Yin, M., Ning, L., Liu, D., & Xue, X. (2015). A threshold artificial neural network model for improving runoff prediction in a karst watershed. Environmental Earth Sciences, 74, 5039-5048. doi:10.1007/s12665-015-4562-9.
Moriasi, D. N., Arnold, J. G., Van Liew, M.W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885−900 doi:10.13031/2013.23153.
Motovilov, Y.G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98–99, 31, 257-277, doi:10.1016/S0168-1923(99)00102-1.
Mubialiwo, A., Abebe, A., & Onyutha, C. (2021). Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment. SN Applied. Sciences, 3, 515, doi:10.1007/s42452-021-04514-7.
Nayak, P.C., Satyaji Rao, Y.R., & Sudheer, K.P. (2006). Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management, 20, 77-90, doi:10.1007/s11269-006-4007-z.
Nourani, V., Baghanam, A.H., Rahimi, A.Y., & Nejad, F.H. (2014). Evaluation of Wavelet-Based De-noising Approach in Hydrological Models Linked to Artificial Neural Networks. Pp. 209-241, In: Islam, T., Srivastava, P.K., Gupta, M., Zhu, X., & Mukherjee, S. (Eds), Computational Intelligence Techniques in Earth and Environmental Sciences, Springer Netherlands, Dordrecht. volume 9789401786, doi:10.1007/978-94-017-8642-3_12.
Onyutha, C. (2019). Hydrological model supported by a stepwise calibration against sub-flows and validation of extreme flow events. Water, 11, 244, https://doi.org/10.3390/w11020244.
Principe, J.C., Euliano, N.R., & Curt Lefebvre, W. (1999). Neural and Adaptive Systems: Fundamentals through Simulations. Wiley, 672 pages.
Panda, R.K., Pramanik, N., & Bala, B., (2010). Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Computers and Geosciences, 36, 735-745, doi:10.1016/j.cageo.2009.07.012.
Pramanik, N., & Panda, R.K. (2009). Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences Journal, 54, 247-260, doi:10.1623/hysj.54.2.247.
Rezaeianzadeh, M., Stein, A., Tabari, H., Abghari, H., Jalalkamali, N., Hosseinipour, E.Z., & Singh, V.P. (2013). Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. International Journal of Environmental Science and Technology, 10, 1181-1192, doi:10.1007/s13762-013-0209-0.
Sen, S.Y., & Ozkurt, N. (2020). Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, 978, Pp. 1-6. doi:10.1109/ASYU50717.2020.9259896.
Solomatine, D.P., & Ostfeld, A. (2008). Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics, 10, 3-22, doi:10.2166/hydro.2008.015.
Taesombat, W., & Sriwongsitanon, N. (2010). Flood investigation in the Upper Ping River Basin using mathematical models. Kasetsart Journal-Natural Science, 44, 152-166.
Tokar, B.A.S., & Johnson, P.A. (1999). Rainfall-Runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 4, 232-239.
Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J. M., Viney, N. R., & Teng, J. (2010). Climate non-stationarity–validity of calibrated rainfall–runoff models for use in climate change studies. Journal of Hydrology, 394(3), 447-457.
Wagena, M.B., Goering, D., Collick, A.S., Bock, E., Fuka, D.R., Buda, A., & Easton, Z.M. (2020). Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. Environmental Modelling and Software, 126. doi:10.1016/j.envsoft.2020.104669.
Wu, C., Zhang, X., Wang, W., Lu, C., Zhang, Y., & Qin, W., et al. (2021). Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model. Science of The Total Environment, 783, 146948, doi:10.1016/j.scitotenv.2021.146948.
Ye, W., Bates, B.C., Viney, N.R., Sivapalan, M., & Jakeman, A.J. (1997). Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments. Water Resources Research, 33, 153–166.
Young, C.C., Liu, W.C., & Wu, M.C. (2017). A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Applied Soft Computing Journal, 53, 205-216, doi:10.1016/j.asoc.2016.12.052.
Zareian, M.J., Eslamian, S., Gohari, A., & Adamowski, J.F. (2017). The effect of climate change on watershed water balance. Pp. 215–238, In: Mathematical Advances towards Sustainable Environmental Systems, Springer.
Zarezade Mehrizi, S., Khoorani, A., Bazrafshan, J., & Bazrafshan, O. (2018). Assessing the efficiency of SWAT model for runoff simulation in Gamasiyab basin. Rangeland and Watershed Management. Iranian Journal of Natural Resources, 70(4), 881-893 (in Persian).
Zeinoddini, S., Anvari, S., & Zahmatkesh, Z. (2018). Application of Simulation-Optimization Approaches to Assess the Effect of Climate and Management Scenarios on a Water Resource System. Iran-Water Resources Research, 14(5), 295-310 (in Persian).
Zhang, G., Patuwo, B., & Hu, M.Y. (2001). A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers & Operations Research, 28, 381-396, doi:10.1016/S0305-0548(99)00123-9.
Zhu, S., Luo, X., Yuan, X., & Xu, Z. (2020). An improved long short-term memory network for streamflow forecasting in the upper Yangtze River. Stochastic Environmental Research and Risk Assessment, 34, 1313-1329, doi:10.1007/s00477-020-01766-4.
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