Flow forecast by SWAT model and ANN in Pracana basin, Portugal
Tóm tắt
Từ khóa
Tài liệu tham khảo
Altunkaynak, 2007, Forecasting surface water level fluctuations of lake van by artificial neural networks, Water Resour Manage, 21, 399, 10.1007/s11269-006-9022-6
Altunkaynak, 2005, Regional streamflow estimation by standard regional dependence function approach, J Hydraul Eng – ASCE, 131, 1001, 10.1061/(ASCE)0733-9429(2005)131:11(1001)
Anctil, 2005, Evaluation of neural network streamflow forecasting on 47 watersheds, J Hydrol Eng, 10, 85, 10.1061/(ASCE)1084-0699(2005)10:1(85)
Arnold J, Williams J, Srinivasan R, King K. SWAT – soil and water assessment tool – documentation and users manual. USDA-ARS, Temple, Texas; 1996.
Arnold, 2005, SWAT2000: current capabilities and research opportunities in applied watershed modelling, Hydrol Process, 19, 563, 10.1002/hyp.5611
ASCE, 2000, Task committee on application of ANNs in hydrology, artificial neural networks in hydrology. I: Preliminary concepts, J Hydrol Eng, 5, 115, 10.1061/(ASCE)1084-0699(2000)5:2(115)
ASCE, 2000, Task committee on application of ANNs in hydrology, artificial neural networks in hydrology. II: Hydrology application, J Hydrol Eng, 5, 124, 10.1061/(ASCE)1084-0699(2000)5:2(124)
Baratti, 2003, River flow forecast for reservoir management through neural networks, Neurocomputing, 55, 421, 10.1016/S0925-2312(03)00387-4
Burlando, 1993, Forecasting of short-term rainfall using ARMA models, J Hydrol, 144, 193, 10.1016/0022-1694(93)90172-6
Calvoa, 2007, Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds, J Hydrol, 332, 1, 10.1016/j.jhydrol.2006.06.015
Can, 2002, A new improved na/k geothermometer by artificial neural networks, Geothermics, 31, 751, 10.1016/S0375-6505(02)00044-5
Chen, 2006, Integration of artificial neural networks with conceptual models in rainfall–runoff modeling, J Hydrol, 318, 232, 10.1016/j.jhydrol.2005.06.017
Demuth, 2001
Di Luzio, 2005, Effect of GIS data quality on small watershed stream flow and sediment simulations, Hydrol Process, 19, 629, 10.1002/hyp.5612
Govender, 2005, Modelling streamflow from two small South African experimental catchments using the SWAT model, Hydrol Process, 19, 683, 10.1002/hyp.5621
Govindaraju, 2000
Hsu, 1995, Artificial neural network modeling of the rainfall runoff process, Water Resour Res, 31, 2517, 10.1029/95WR01955
Jha, 2004, Impacts of climate change on streamflow in the upper Mississippi river basin: a regional climate model perspective, J Geophys Res, 10.1029/2003JD003686
Kahya, 1993, US streamflow patterns in relation to the El Nino/southern oscillation, Water Resour Res, 28, 2491, 10.1029/93WR00744
Karabork, 1999, Multivariate stochastic modeling of streamflows in the Sakarya basin, Turkish J Eng Environ Sci, 23, 133
Kaur, 2003, Assessment of SWAT model for soil and water management in India. Land use, Water Resour Res, 3, 1
Kisi, 2004, Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation, Hydrol Sci J, 49, 1025, 10.1623/hysj.49.6.1025.55720
Kisi, 2006, Evapotranspiration estimation using feed-forward neural networks, Nordic Hydrol, 37, 247, 10.2166/nh.2006.010
Kisi, 2007, Streamflow forecasting using different artificial neural network algorithms, J Hydrol Eng, 12, 532, 10.1061/(ASCE)1084-0699(2007)12:5(532)
Kisi, 2008, River flow forecasting and estimation using different artificial neural network techniques, Hydrol Res, 39, 27, 10.2166/nh.2008.026
Lee, 2007, Predictions of rainfall–runoff response and soil moisture dynamics in a micro-scale catchment using the crew model, Hydrol Earth Syst Sci, 11, 819, 10.5194/hess-11-819-2007
Maier, 2000, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ Modell Software, 15, 101, 10.1016/S1364-8152(99)00007-9
Markus M. Application of neural networks in streamflow forecasting. Ph.D. thesis, Colorado State University; 1997.
Moon, 2004, Stream flow estimation using spatially distributed rainfall in the trinity river basin, Texas, Trans ASAE, 47, 1445, 10.13031/2013.17624
Morid S, Gosain AK, Keshari AK. Comparison of the SWAT model and ANN for daily simulation of runoff in snowbound ungauged catchments. In: Fifth international conference on hydroinformatics, Cardiff, UK; 2002.
Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0
Salas, 2000, Streamflow forecasting based on artificial neural networks, 23
Silverman, 2000, Artificial neural networks and long-lead precipitation prediction in California, J Appl Meteorol, 31, 57, 10.1175/1520-0450(2000)039<0057:ANNALR>2.0.CO;2
Sivakumar, 2002, River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches, J Hydrol, 265, 225245
Srinivasan, 1994, Integration of a basin-scale water quality model with GIS, Water Resour Bull, 30, 453, 10.1111/j.1752-1688.1994.tb03304.x
Srivastava, 2006, Comparison of process-based and artificial neural network approaches for streamflow modelling in an agricultural watershed, J Am Water Resour Assoc, 42, 545, 10.1111/j.1752-1688.2006.tb04475.x
Tokar, 2000, Precipitation–runoff modelling using artificial neural networks and conceptual models, J Hydrol Eng, 4, 232, 10.1061/(ASCE)1084-0699(1999)4:3(232)
Toth, 2000, Comparison of short-term rainfall prediction models for real-time flood forecasting, J Hydrol, 239, 132, 10.1016/S0022-1694(00)00344-9
Venancio A, Martins F, Chambel P, Neves R. Modelacao hidrologica da bacia drenante da albufeira de pracana Faro: V Congresso Iberico; 4–8 December, 2006.
Wu, 2005, Artificial neural networks for forecasting watershed runoff and stream flows, J Hydrol Eng, 10, 216, 10.1061/(ASCE)1084-0699(2005)10:3(216)
Zealand, 1999, Short term streamflow forecasting using artificial neural networks, J Hydrol, 214, 3248