Entropy-based neural networks model for flow duration curves at ungauged sites

Journal of Hydrology

Volumn 529, Issue , 2015, Pages 1007-1020

  Atieh, Maya ^a   Gharabaghi, Bahram ^a   Rudra, Ramesh ^a  

^a UNIVERSITY OF GUELPH   (Canada)

Author keywords

Apportionment entropy disorder index; Artificial neural networks; Flow duration curve; Ungauged sites

Indexed keywords

ENTROPY; LOCATION; WATERSHEDS;

APPORTIONMENT ENTROPIES; FLOW DURATION CURVE; LOCATION AND SCALE PARAMETERS; LOG-NORMAL DISTRIBUTION; MEAN ANNUAL PRECIPITATION; TEMPORAL AND SPATIAL VARIABILITY; UNGAUGED SITES; WATERSHED CHARACTERISTICS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BASEFLOW; DATA SET; ENTROPY; EVAPOTRANSPIRATION; FLOW PATTERN; GAUGE; HEADWATER; HYDROELECTRIC POWER; INDEX METHOD; MODEL VALIDATION; PRECIPITATION (CLIMATOLOGY); SPATIOTEMPORAL ANALYSIS; STREAM; WATERSHED;

CANADA; ONTARIO [CANADA];

EID: 84942279402     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2015.08.068     Document Type: Article

References (94)

1. Akaike H. Information theory as an extension of the maximum likelihood principle. Second International Symposium on Information Theory 1973, 267-281. Akademiai Kiado, Budapest. B.N. Petrov, F. Csaki (Eds.).
2. Arnold C.L., Gibbons G.C. Impervious surface coverage: the emergence of a key environmental indicator. J. Am. Plann. Assoc. 1996, 62(2):243-259.
3. ASCE Artificial neural networks in hydrology I: preliminary concepts. J. Hydrol. Eng. 2000, 5(2):115-123.
4. Archfield S., Vogel R. Map correlation method: selection of a reference stream gauge to estimate daily stream flow at ungauged catchments. Water Resour. Res. 2010, 46(10):W10513.
5. Archfield, S., Vogel, R., Steeves, P., et al., 2010. The Massachusetts Sustainable-Yield Estimator: A Decision-support Tool to Assess Water Availability at Ungauged Sites in Massachusetts. U.S. Geological Survey Scientific Investigations Report, 2009-5227.
6. Archfield S.A., Steeves P.A., Guthrie J.D., Rie K.G. Towards a publicly available, map-based regional software tool to estimate unregulated daily streamflow at ungauged rivers. Geosci. Model Dev. 2013, 6:101-115.
7. Ahmed S., Singh A., Rudra R., Gharabaghi B. Comparison of CANWET and HSPF for water budget and water quality modeling in rural Ontario. Water Qual. Res. J. Can. 2013, 49(1):53-71.
8. Asnaashari A., Gharabaghi B., McBean E., Mahboubi A.A. Reservoir management under predictable climate variability and change. J. Water Clim. Change 2015, 10.2166/wcc.2015.053.
9. Bowden G.J., Dandy G.C., Maier H.R. Input determination for neural network models in water resources applications. Part 1-background and methodology. J. Hydrol. 2005, 301:75-92.
10. Besaw L.E., Donna M.R., Bierman P.R., William R.H. Advances in ungauged streamflow prediction using artificial neural networks. J. Hydrol. 2010, 386(1-4):27-37.
11. Boyer C., Chaumont D., Chartier I., Roy A.G. Impact of climate change on the hydrology of St. Lawrence tributaries. J. Hydrometeorol. 2010, 384(1-2):65-83.
12. Booker D., Snelder T. Comparing methods for estimating flow duration curves at ungauged sites. J. Hydrol. 2012, 434-435:78-94.
13. Castellarin A., Galeati G., Brandimarte L. Regional flow duration curves: reliability for ungauged basins. Adv. Water Resour. 2004, 27(10):953-965.
14. Cheng Q., Koa C., Yuana Y., Gea Y., Zhang S. GIS modeling for predicting river runoff volume in ungauged drainages in the Greater Toronto Area, Canada. Comput. Geosci. 2006, 32(8):1108-1119.
15. Coulibaly P. Spatial and temporal variability of Canadian seasonal precipitation (1900-2000). Adv. Water Resour. 2006, 29(12):1846-1865.
16. Castellarin A., Camorani G., Brath A. Predicting annual and long-term flow-duration curves in ungauged basins. Adv. Water Resour. 2007, 30(4):937-953.
17. Castellarin A. Regional prediction of flow duration curves using a three dimensional kriging. J. Hydrol. 2014, 513:179-191.
18. Casadei S., Liucci L., Valigi D. Hydrological uncertainty and hydropower: new methods to optimize the performance of the plant. Energy Procedia 2014, 59:263-269.
19. Chapi K., Rudra R., Ahmed S., Khan A., Gharabaghi B. Spatial-temporal dynamics of runoff generation areas in a small agricultural watershed in Southern Ontario. J. Water Resour. Prot. 2014, 7(1):14-40.
20. Demirel M., Venancio A., Kahya E. Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv. Eng. Softw. 2009, 40(7):467-473.
21. EC, 2012. Environment Canada Climate Normals (1981-2010). (accessed September 2014). http://climate.weather.gc.ca/climate_normals/index_e.html.
22. EC, 2014. Environment Canada Adjusted Precipitation Data. (accessed November 2014). http://climate.weather.gc.ca/climate_normals/index_e.html.
23. ECDE, 2012. Environment Canada Data Explorer: HYDAT Version 1.0. (accessed September 2013). https://ec.gc.ca/rhc-wsc/default.asp?lang=En%26n=0A47D72F-1.
24. Farmer H.W., Vogel R. Performance-weighted methods for estimating monthly streamflow at ungauged sites. J. Hydrol. 2013, 477:240-250.
25. Holmes M.G.R., Young A.R., Gustard A., Grew R. A region of influence approach to predicting flow duration curves within ungauged catchments. Hydrol. Earth Syst. Sci. 2002, 6(4):721-731.
26. Hu T.S., Wu F.Y., Zhang X. Rainfall-runoff modeling using principal component analysis and neural network. Nordic Hydrol. 2007, 38(3):235-248.
27. Hao P., Singh V.P. Single-site monthly stream flow simulation using entropy theory. Water Resour. Res. 2011, 47(9):W09528.
28. Hrachowitz M., Savenije H.H.G., Blöschl G., McDonnell J.J., et al. A decade of predictions in ungauged basins (PUB)-a review. Hydrol. Sci. J. 2013, 58(6):1198-1255.
29. Hashmi M., Shamseldin A. Use of gene expression programming in regionalization of flow duration curve. Adv. Water Resour. 2014, 68:1-12.
30. Isik S., Kalin L., Schoonover J.E., Srivastava P., Lockab B. Modeling effects of changing land cover/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. J. Hydrol. 2013, 485:103-112.
31. Kawachi T., Maruyama T., Singh V.P. Rainfall entropy for delineation of water resources zones in Japan. J. Hydrol. 2001, 246:36-44.
32. Kingston G.B., Lambert M.F., Maier H.R. Bayesian training of artificial neural networks used for water resources modeling. Water Resour. Res. 2005, 41(12):W12409.
33. Khan M., Coulibaly P. Bayesian neural network for rainfall-runoff modeling. Water Resour. Res. 2006, 42(7):W07409.
34. Khan M., Coulibaly P. Assessing hydrologic impact of climate change with uncertainty estimates: Bayesian neural network approach. J. Hydrometeorol. 2010, 11:482-495.
35. Khosravi A., Nahavandi S., Creighton D., Atiya A. Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans. Neural Netw. 2011, 22(9):1341-1356.
36. Kalteh M. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput. Geosci. 2013, 54:1-8.
37. Liong S.Y., Gautam T.R., Muttil N., Khu S.T. Runoff Forecasting with Genetic Programming. Hydroinformatics 2000 2000, IOWA, USA.
38. Li M., Shao Q., Zhang L., Chiew F. A new regionalization approach and its application to predict flow duration curve in ungauged basins. J. Hydrol. 2010, 389:135-145.
39. Lyons, M., Lubitz, D., 2013. Archimedes screws for micro-hydropower generation. In: Engineering and Technology Conference. Proceedings of the ASME 2013 7th International Conference on Energy Sustainability and 11th Fuel Cell Science.
40. Liu Y., Yang W., Yu Z., Lung I., Gharabaghi B. Estimating sediment yield from upland and channel erosion at a watershed scale using SWAT. Water Resour. Manage. 2015, 29(5):1399-1412.
41. Merz R., Bloschl G. Regionalisation of catchment model parameters. J. Hydrol. 2004, 287(1-4):95-123.
42. Metcalfe R.A., Chang C., Smakhtin V. Tools to support the implementation of environmentally sustainable flow regimes at Ontario's waterpower facilities. Can. Water Resour. J. 2005, 30(2):97-110.
43. Mishra A.K., Ozger M., Singh V.P. An entropy-based investigation into the variability of precipitation. J. Hydrol. 2009, 370(1-4):139-154.
44. Makkearson A., Chang N.B., Zhou X. Short term streamflow forecasting with global climate change implications - a comparative study between genetic programming and neural network models. J. Hydrol. 2008, 352:336-354.
45. Mohamoud Y.M. Prediction of daily flow duration curves and streamflow for ungauged catchments using regional flow duration curves. Hydrol. Sci. J. 2008, 53(4):706-724.
46. Mishra A.K., Coulibaly P. Hydrometric network evaluation for Canadian watersheds. J. Hydrol. 2010, 380(3-4):420-437.
47. Maier H., Jain A., Dandy G., Sudheer K. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ. Modell. Softw. 2010, 25(8):891-909.
48. Moramarco T., Singh V.P. Formulation of the entropy parameter based on hydraulic and geometric characteristics of river cross sections. J. Hydrol. Eng. 2010, 15(10):852-858.
49. Masih I., Uhlenbrook S., Maskey S., Ahmad M.D. Regionalization of a conceptual rainfall-runoff model based on similarity of the flow duration curve: a case study from the semi-arid Karkheh basin, Iran. J. Hydrol. 2010, 391(1-2):188-201.
50. Mcmanamay R., Orth D., Dolloff C. Revisiting the homogenization of dammed rivers in the southeastern US. J. Hydrol. 2012, 424-425:217-237.
51. Mondal S., Jana S., Majumder S., Roy D. A comparative study for prediction of direct runoff for a river basin using geomorphological approach and artificial neural networks. Appl. Water Sci. 2012, 2(1):1-13.
52. Mendicino G., Senatore A. Evaluation of parametric and statistical approaches for the regionalization of flow duration curves in intermittent regimes. J. Hydrol. 2013, 480:19-32.
53. MNR, 2013. Ontario Flow Assessment Tool Version III Manual. Ministry of Natural Resources.
54. Muller M.F., Dralle D.N., Thompson S.E. Analytical model for flow duration curves in seasonally dry climates. Water Resour. Res. 2014, 50(7):5510-5531.
55. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models part I-a discussion of principles. J. Hydrol. 1970, 10(3):282-290.
56. Newcombe R.G. Two sided confidence intervals for the single proportion: comparison of seven methods. Stat. Med. 1998, 17(8):857-872.
57. Neff B.P., Day S.M., Piggot A.R., Fuller L.M. Baseflow in the Great Lakes Basin 2005, National Water Research Institute, Environment Canada, Burlington, Ontario, Canada.
58. NeuralTools Version 6 by Palisade Corporation. . http://www.palisade.com/neuraltools/.
59. Oudin L., Andréassian V., Perrin C., Michel C., Le Moine N. Spatial proximity, physical similarity, regression and ungauged catchments: a comparison of regionalization approaches based on 913 French catchments. Water Resour. Res. 2008, 44(3):W03413.
60. Ontario Flow Assessment Tools III, 2013. Ministry of Natural Resources and Forestry. (accessed June 2014). http://www.giscoeapp.lrc.gov.on.ca/web/mnr/wrip/ofat/Viewer/viewer.html.
61. Piggott A.R., Moin S., Southam C. A revised approach to UKIH method for calculation of baseflow. Hydrol. Sci. J. 2005, 50(5):911-920.
62. Piggott, A.R., Sharpe, D.R., 2007. Geological interpretations of baseflow for southern Ontario 394-401. In: Proceedings of the 60th Canadian Geotechnical and 8th Joint IAH-CNC and CGS Groundwater Specialty Conferences. Canadian Geotechnical Society and Canadian National Chapter of the International Association of Hydrogeologists.
63. Piotrowski A.P., Napiorkowski J.J. Optimizing neural networks for river flow forecasting - evolutionary computation methods versus the Levenberg-Marquardt approach. J. Hydrol. 2011, 407(1-4):12-27.
64. Pandey A., Prasad R., Srivastava J., Pandey S. Retrieval of soil moisture by artificial neural networks using X-band ground based data. Russ. Agric. Sci. 2012, 38(3):230-233.
65. Piotrowski A.P., Napiorkowski J.J. A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modeling. J. Hydrol. 2013, 476:97-111.
66. Ries III, K.G., Guthrie, J.G., Rea, A.H., Steeves, P.A., Stewart, D.W., 2008. StreamStats: A Water Resources Web Application. U.S. Geological Survey Fact Sheet 2008-3067. . http://pubs.usgs.gov/fs/2008/3067/.
67. Rojanamon P., Chaisomphob T., Rattanapitikon W. Regional flow duration model for the Salawin river basin of Thailand. Sci. Asia 2007, 33(4):411-419.
68. Razavi T., Coulibaly P. Streamflow prediction in ungauged basins: review of regionalization methods. J. Hydrol. Eng. 2013, 18(8):958-975.
69. Rianna M., Efstratiadis A., Russo F., Napolitano F., Koutsoyiannis D. A stochastic index method for calculating annual flow duration curves in intermittent rivers. Irrig. Drain. 2013, 62(S2):41-49.
70. Searcy, J.K., 1959. Flow Duration Curves. Manual of Hydrology: Part 2. Low-Flow Techniques. Methods and Practices of the Geological Survey. Geological Survey Water-Supply Paper 1542-A.
71. Specht D.F. A general regression neural network. IEEE Trans. Neural Netw. 1991, 2(6):568-576.
72. Singh V.P. The use of entropy in hydrology and water resources. Hydrol. Process. 1997, 11(6):587-626.
73. Sivaplan M., Takeuchi K., Franks S.W., Gupta V.K., Karambiri H., et al. IAHS decade on predictions in ungauged basins (PUB), 2003-2012: shaping an exciting future for the hydrological sciences. Hydrol. Sci. J. 2003, 48(6):857-880.
74. Santhi C., Allen P.M., Muttiah R.S., Arnold J.G., Tuppad P. Regional estimation of baseflow for conterminous United States by hydrology landscape regions. J. Hydrol. 2007, 351(1-2):139-153.
75. Skoien J.O., Bloschl G. Spatiotemporal topological kriging of runoff time series. Water Resour. Res. 2007, 43(9):W09419.
76. Srivastav R.K., Sudheer K.P., Chaubey I.A. Simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour. Res. 2007, 43(10):W10407.
77. Solomatine D., Shrestha D. A novel method to estimate model uncertainty using machine learning techniques. Water Resour. Res. 2009, 45(12):W00B11.
78. Saliha A., Awulachew S., Cullmann J., Horlacher Hans-B. Estimation of flow in ungauged catchments by coupling a hydrological model and neural networks: case study. Hydrol. Res. 2011, 42(5):386-400.
79. Samuel J., Coulibaly P., Metcalfe R.A. Estimation of continuous streamflow in Ontario ungauged basins: comparison of regionalization methods. J. Hydrol. Eng. 2011, 16(5):447-459.
80. Shu C., Ouarda T. Improved methods for daily streamflow estimates at ungauged sites. Water Resour. Res. 2012, 48(2):WO2523.
81. Singh V.P. Entropy Theory and its Applications in Environmental and Water Engineering 2013, John Wiley, New York.
82. Sabouri F., Gharabaghi B., Mahboubi A., Mcbean E. Impervious surfaces and sewer pipe effects on stormwater runoff temperature. J. Hydrol. 2013, 502:10-17.
83. Sattar A., Gharabaghi B. Gene expression models for prediction of longitudinal dispersion coefficients in streams. J. Hydrol. 2015, 524:587-596.
84. Talebizadeh M., Ayyoubzadeh S., Ghasemzadeh M. Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resour. Manage. 2010, 24(9):1747-1761.
85. Trenouth W., Gharabaghi B. Event based soil loss models for construction sites. J. Hydrol. 2015, 524:780-788.
86. Vogel R.M., Fennessey N.M. Flow duration curves I: new interpretation and confidence intervals. J. Water Res. Plann. Manage. 1994, 120(4):485-504.
87. Vose D. Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modeling 1996, John Wiley, New York, 317.
88. Vicente S., Saz Sergio M., Sánchez M.A., Cuadrat J.M. Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Clim. Res. 2003, 24(2):161-180.
89. Walker H. Studies in the History of the Statistical Method 1931, 24-25. Williams and Wilkins Co., Baltimore, MD.
90. Warnick C.C. Hydropower Engineering 1984, 59-73. Prentice-Hall Inc., Englewood Cliffs, New Jersey.
91. Wei X.H., Zhang M.F. Quantifying streamflow change caused by forest disturbance at a large spatial scale: a single watershed study. Water Resour. Res. 2010, 46(12):W12525.
92. Wu C.L., Chau K.W. Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J. Hydrol. 2011, 399(3-4):394-409.
93. Zhang X., Srinivasan R. GIS-based spatial precipitation estimation: a comparison of geostatistical approaches. J. Am. Water Resour. Assoc. 2009, 45(4):894-906.
94. Zhao F., Zhang L., Xu Z., Scott D.F. Evaluation of methods for estimating the effects of vegetation change and climate variability on streamflow. Water Resour. Res. 2010, 46(3):W03505.