Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds

Aichouri, 2015, River flow model using artificial neural networks, Energy Proc., 74, 1007, 10.1016/j.egypro.2015.07.832

Alvarenga, 2016, Assessment of land cover change on the hydrology of a Brazilian headwater watershed using the Distributed Hydrology-Soil-Vegetation Model, CATENA, 143, 7, 10.1016/j.catena.2016.04.001

Alvares, 2013, Köppen’s climate classification map for Brazil, Meteorol. Zeitschrift, 22, 711, 10.1127/0941-2948/2013/0507

Baratti, 2003, River flow forecast for reservoir management through neural networks, Neurocomputing, 55, 421, 10.1016/S0925-2312(03)00387-4

Behzad, 2009, Generalization performance of support vector machines and neural networks in runoff modeling, Expert Syst. Appl., 36, 7624, 10.1016/j.eswa.2008.09.053

Besaw, 2010, Advances in ungauged streamflow prediction using artificial neural networks, J. Hydrol., 386, 27, 10.1016/j.jhydrol.2010.02.037

Blume, 2007, Rainfall-runoff response, event-based runoff coefficients and hydrograph separation, Hydrol. Sci. J., 52, 843, 10.1623/hysj.52.5.843

Carcano, 2008, Jordan recurrent neural network versus IHACRES in modelling daily streamflows, J. Hydrol., 362, 291, 10.1016/j.jhydrol.2008.08.026

Chang, 2014, Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control, J. Hydrol., 517, 836, 10.1016/j.jhydrol.2014.06.013

Danandeh Mehr, 2015, Successive-station monthly streamflow prediction using different artificial neural network algorithms, Int. J. Environ. Sci. Technol., 12, 2191, 10.1007/s13762-014-0613-0

Dariane, 2018, Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection, J. Hydroinformatics, 20, 520, 10.2166/hydro.2017.076

Dias, 2015, Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazil, J. Hydrol. Reg. Stud., 4, 108, 10.1016/j.ejrh.2015.05.010

Elsafi, 2014, Artificial Neural Networks (ANNs) for flood forecasting at Dongola Station in the River Nile, Sudan, Alexandria Eng. J., 53, 655, 10.1016/j.aej.2014.06.010

Ferreira, 2015, Responses of bees to habitat loss in fragmented landscapes of Brazilian Atlantic Rainforest, Landsc. Ecol., 30, 2067, 10.1007/s10980-015-0231-3

Hagan, 1994, Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Networks, 5, 989, 10.1109/72.329697

Hasanpour Kashani, 2014, Comparison of volterra model and artificial neural networks for rainfall-runoff simulation, Nat. Resour. Res., 23, 341, 10.1007/s11053-014-9235-y

Haykin, 1999

Huo, 2012, Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China, J. Hydrol., 420–421, 159, 10.1016/j.jhydrol.2011.11.054

Instituto Brasileiro de Geografia e Estatística, 2012

Jain, 2007, Hybrid neural network models for hydrologic time series forecasting, Appl. Soft Comput., 7, 585, 10.1016/j.asoc.2006.03.002

Kagoda, 2010, Application of radial basis function neural networks to short-term streamflow forecasting, Phys. Chem. Earth, Parts A/B/C, 35, 571, 10.1016/j.pce.2010.07.021

Kar, 2015, Assessing unit hydrograph parameters and peak runoff responses from storm rainfall events: a case study in Hancheon Basin of Jeju Island, J. Environ. Sci. Int., 24, 437, 10.5322/JESI.2015.24.4.437

Kişi, 2007, Streamflow forecasting using different artificial neural network algorithms, J. Hydrol. Eng., 12, 532, 10.1061/(ASCE)1084-0699(2007)12:5(532)

Lamichhane, 2017, Development of flood warning system and flood inundation mapping using field survey and LiDAR data for the Grand River near the city of Painesville, Ohio, Hydrology, 4, 24, 10.3390/hydrology4020024

Lin, 2008, A systematic approach to the input determination for neural network rainfall–runoff models, Hydrol. Process., 22, 2524, 10.1002/hyp.6849

Lin, 2018, Surface runoff response to climate change based on artificial neural network (ANN) models: A case study with Zagunao catchment in Upper Minjiang River, Southwest China, J. Water Clim. Chang., 9, jwc2018130

Maier, 2000, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ. Model. Softw., 15, 101, 10.1016/S1364-8152(99)00007-9

Marques, M., Costa, M.F. da, Mayorga, M.I. de O., Pinheiro, P.R.C., 2004. Water environments: anthropogenic pressures and ecosystem changes in the Atlantic drainage basins of Brazil. AMBIO A J. Hum. Environ. 33, 68–77. doi: 10.1579/0044-7447-33.1.68.

McGuire, 2006, A review and evaluation of catchment transit time modeling, J. Hydrol., 330, 543, 10.1016/j.jhydrol.2006.04.020

Melo Neto, J. de O., Silva, A.M. da, Mello, C.R. de, Méllo Júnior, A.V., 2014. Simulação hidrológica escalar com o modelo SWAT. Rev. Bras. Recur. Hídricos 19, 177–188.

Mendes, H. de A., 2016. Metodologia para calibração do modelo hidrológico DHSVM. Universidade Federal do Espírito Santo.

Miranda, 2014, Métodos de separação dos escoamentos superficial direto e subterrâneo: estudo de caso para a Bacia do Rio das Velhas, Rev. Bras. Recur. Hídricos, 19, 169

Moreira, M.C., Oliveira, T.E.C. de, Cecílio, R.A., Pinto, F. de A.C., Pruski, F.F., 2016. Spatial Interpolation of Rainfall Erosivity Using Artificial Neural Networks for Southern Brazil Conditions. Rev. Bras. Ciência do Solo 40. doi: 10.1590/18069657rbcs20150132.

Noori, 2016, Coupling SWAT and ANN models for enhanced daily streamflow prediction, J. Hydrol., 533, 141, 10.1016/j.jhydrol.2015.11.050

Nourani, 2011, Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process, J. Hydrol., 402, 41, 10.1016/j.jhydrol.2011.03.002

Persson, 2002, Predicting the dielectric constant-water content relationship using artificial neural networks, Soil Sci. Soc. Am. J., 66, 1424, 10.2136/sssaj2002.1424

Price, 2011, Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: A review, Prog. Phys. Geogr., 35, 465, 10.1177/0309133311402714

Ray, 2016, Influence of time discretization and input parameter on the ANN based synthetic streamflow generation, Water Resour. Manag., 30, 4695, 10.1007/s11269-016-1448-x

Rezaeian-Zadeh, 2010, Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions, Water Resour. Manag., 24, 2673, 10.1007/s11269-009-9573-4

Rezaeian-Zadeh, 2013, Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting, Int. J. Environ. Sci. Technol., 10, 1181, 10.1007/s13762-013-0209-0

Rezaeian-Zadeh, 2013, Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions, Arab. J. Geosci., 6, 2529, 10.1007/s12517-011-0517-y

Ribeiro, 2009, The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation, Biol. Conserv., 142, 1141, 10.1016/j.biocon.2009.02.021

Scarano, 2015, Brazilian Atlantic forest: impact, vulnerability, and adaptation to climate change, Biodivers. Conserv., 24, 2319, 10.1007/s10531-015-0972-y

Shiau, 2016, Suitability of ANN-based daily streamflow extension models: a case study of Gaoping River Basin, Taiwan, Water Resour. Manag., 30, 1499, 10.1007/s11269-016-1235-8

Thier, 2016, Floristic composition and edge-induced homogenization in tree communities in the fragmented Atlantic rainforest of Rio De Janeiro, Brazil, Trop. Conserv. Sci., 9, 852, 10.1177/194008291600900217

Toth, 2016, Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions, Hydrol. Earth Syst. Sci., 20, 2383, 10.5194/hess-20-2383-2016

Veintimilla-Reyes, 2016, Artificial neural networks applied to flow prediction: a use case for the Tomebamba river, Proc. Eng., 162, 153, 10.1016/j.proeng.2016.11.031

Vertessy, 1993, Predicting water yield from a mountain ash forest catchment using a terrain analysis based catchment model, J. Hydrol., 150, 665, 10.1016/0022-1694(93)90131-R

Wigmosta, 1994, A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30, 1665, 10.1029/94WR00436

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)

Xavier, 2015, Daily gridded meteorological variables in Brazil (1980–2013), Int. J. Climatol., 36, n/a-n/a

Yaseen, 2015, Artificial intelligence based models for stream-flow forecasting: 2000–2015, J. Hydrol., 530, 829, 10.1016/j.jhydrol.2015.10.038

Zhang, 1998, Forecasting with artificial neural networks: The state of the art, Int. J. Forecast., 14, 35, 10.1016/S0169-2070(97)00044-7

Zhang, 2018, How can streamflow and climate-landscape data be used to estimate baseflow mean response time?, J. Hydrol., 557, 916, 10.1016/j.jhydrol.2017.12.070

Zhu, 2017, Permeability prediction of the tight sandstone reservoirs using hybrid intelligent algorithm and nuclear magnetic resonance logging data, Arab. J. Sci. Eng., 42, 1643, 10.1007/s13369-016-2365-2

Zhu, 2017, Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data, Interpretation, 5, T341, 10.1190/INT-2016-0188.1

Zhu, 2018, Prediction of total organic carbon content in shale reservoir based on a new integrated hybrid neural network and conventional well logging curves, J. Geophys. Eng., 15, 1050, 10.1088/1742-2140/aaa7af

Zhu, 2019, Forming a new small sample deep learning model to predict total organic carbon content by combining unsupervised learning with semisupervised learning, Appl. Soft Comput., 83, 10.1016/j.asoc.2019.105596

Zhu, 2018, Application of Multiboost-KELM algorithm to alleviate the collinearity of log curves for evaluating the abundance of organic matter in marine mud shale reservoirs: a case study in Sichuan Basin, China, Acta Geophys., 66, 983, 10.1007/s11600-018-0180-8