Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection

Journal of Hydrology

Volumn 400, Issue 1-2, 2011, Pages 10-23

  He, Jianxun ^a   Valeo, Caterina ^a   Chu, Angus ^a   Neumann, Norman F ^b  

^a UNIVERSITY OF CALGARY   (Canada)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

ANN; Input selection; Partial mutual information; Storm event; Water quality; Water quantity

Indexed keywords

ANN; INPUT SELECTION; PARTIAL MUTUAL INFORMATION; STORM EVENT; WATER QUANTITY;

BIOCHEMICAL OXYGEN DEMAND; DISSOLVED OXYGEN; FORECASTING; LINEAR REGRESSION; MODELS; NEURAL NETWORKS; RUNOFF; SENSITIVITY ANALYSIS; STORM SEWERS; STORMS; TURBIDITY; WATER POLLUTION; WATER QUALITY;

RAIN;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DISCHARGE; DISSOLVED OXYGEN; LINEARITY; NONLINEARITY; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PH; RAINFALL; RAINFALL-RUNOFF MODELING; REGRESSION ANALYSIS; RUNOFF; SENSITIVITY ANALYSIS; STORMWATER; TURBIDITY; WATER QUALITY; WATER TEMPERATURE;

EID: 79952488657     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2011.01.024     Document Type: Article

References (51)

1. Abrahart R.J., See L. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol. Process. 2000, 14(11-12):2157-2172.
2. Amari S., Murata N., Muller K.-R., Finke M., Yang H.H. Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans. Neural Networks 1997, 8(5):985-996.
3. Bannerman R.T., Owens D.W., Dodds R.B., Hornewer N.J. Sources of pollutants in wisconsin stormwater. Water Sci. Technol. 1993, 28(3-5):241-259.
4. Bowden G.J., Maier H.R., Dandy G.C. Input determination for neural network models in water resources applications. Part 2: Case study: forecasting salinity in a river. J. Hydrol. 2005, 301(1-4):93-107.
5. Bruen M., Yang J. Combined hydraulic and black-box models for flood forecasting in urban drainage systems. J. Hydrol. Eng. 2006, 11(6):589-596.
6. Campolo M., Andreussi P., Soldati A. River flood forecasting with a neural network model. Water Resour. Res. 1999, 35(4):1191-1197.
7. Castellano G., Fanelli A.M., Pelillo M. An iterative pruning algorithm for feedforward neural networks. IEEE Trans. Neural Networks 1997, 8(3):519-531.
8. Chibanga R., Berlamont J., Vandewalle J. Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin. Hydrol. Sci. J. 2003, 48(3):363-379.
9. Corbett C.W., Wahl M., Porter D.E., Edwards D., Moise C. Nonpoint source runoff modeling: a comparison of forested watershed and an urban watershed on the South Carolina coast. J. Exp. Mar. Biol. Ecol. 1997, 213(1):133-149.
10. Dawson C.W., Wilby R.L. An artificial neural network approach to rainfall-runoff modeling. Hydrol. Sci. J. 1998, 43(1):47-66.
11. Dawson C.W., Wilby R.L. Hydrological modeling using artificial neural networks. Prog. Phys. Geogr. 2001, 25(1):80-108.
12. Faraway J., Chatfield C. Time series forecasting with neural networks: a comparative study using the airline data. J. Roy. Stat. Soc. Ser. C Appl. Stat. 1998, 47(2):231-250.
13. Fernando D.A.K., Jayawardena A.W. Runoff forecasting using RBF networkworks with OLS algorithm. J. Hydrol. Eng. 1998, 3(3):203-209.
14. Gong N., Denoeux T., Bertrand-Krajewski J.-L. Neural networks for solid transport modeling in sewer systems during storm events. Water Sci. Technol. 1996, 33(9):85-92.
15. Govindaraju R.S. Artificial neural networks in hydrology. II: Hydrologic applications. J. Hydrol. Eng. 2000, 5(2):124-137.
16. Hagan M.T., Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 1994, 5(6):989-993.
17. Harrold T.I., Sharma A., Sheather S. Selection of a kernel bandwidth for measuring dependence of hydrologic time series using the mutual information criterion. Stoch. Environ. Res. Risk Assess 2001, 15(4):310-324.
18. Haykin S.S. Neural Networks: A Comprehensive Foundation 1999, Prentice Hall.
19. He J., Valeo C., Chu A., Neumann N.F. Characteristics of suspended solids, microorganisms, and chemical water quality in event-based stormwater runoff from an urban residential area. Water Environ. Res. 2010, 82(12):2333-2345.
20. He J., Valeo C., Chu A., Neumann N.F. Characterizing physicochemical quality of storm-water runoff from an urban area in Calgary, Alberta. J. Environ. Eng. 2010, 136(11):1206-1217.
21. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Networks 1991, 4(2):251-257.
22. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2(5):359-366.
23. Hsu K.-L., Gupta H.V., Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 1995, 31(10):2517-2530.
24. Imrie C.E., Durucan S., Korre A. River flow prediction using artificial neural network: generalisation beyond the calibration range. J. Hydrol. 2000, 233(1-4):138-153.
25. Jain A., Prasad Indurthy S.K.V. Comparative analysis of event-based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks. J. Hydrol. Eng. 2003, 8(2):93-98.
26. Kim T.-W., Valdes J.B. Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J. Hydrol. Eng. 2003, 8(6):319-328.
27. Kuligowski R.J., Barros A.P. Experiments in short-term precipitation forecasting using artificial neural networks. Monthly Weather Rev. 1998, 126(2):470-482.
28. Lek S., Guiresse M., Giraudel J.-L. Predicting stream nitrogen concentration from watershed features using neural networks. Water Res. 1999, 33(16):3469-3478.
29. Lin B., Kashefipour S.M., Falconer R.A. Predicting near-shore coliform bacteria concentrations using ANNs. Water Sci. Technol. 2003, 48(10):225-232.
30. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Modell. Softw. 2000, 15(1):101-124.
31. Maier H.R., Dandy G.C. The use of artificial neural networks for the prediction of water quality parameters. Water Resour. Res. 1996, 32(4):1013-1022.
32. Maier H.R., Dandy G.C. Neural network based modeling of environmental variables: a systematic approach. Math. Comput. Modell. 2001, 33(6-7):669-682.
33. May R.J., Dandy G.C., Maier H.R., Nixon J.B. Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ. Modell. Softw. 2008, 23(10-11):1289-1299.
34. May R.J., Maier H.R., Dandy G.C., Gayani Fernando T.M.K. Non-linear variable selection for artificial neural networks using partial mutual information. Environ. Modell. Softw. 2008, 23(10-11):1312-1326.
35. McIntyre N., Al-Qurashi A. Performance of the rainfall-runoff models applied to an arid catchment in Oman. Environ. Modell. Softw. 2009, 24(6):726-738.
36. Minns A.W., Hall M.J. Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 1996, 41(3):399-417.
37. Obropta C.C., Kardos J.S. Review of urban stormwater quality models: Deterministic, stochastic, and hybrid approaches. J. Am. Water Resour. Assoc. 2007, 43(6):1508-1523.
38. Priddy Kevin L., Keller Paul E. Artificial Neural Networks: An Introduction 2006, SPIE Press.
39. Rai R.K., Mathur B.S. Event-based sediment yield modeling using artificial neural network. Water Resour. Manage. 2008, 22(4):423-441.
40. Ranjithan S., Eheart J.W., Garrett J.H. Neural network based screening for groundwater reclamation under uncertainty. Water Resour. Res. 1993, 29(3):563-574.
41. Rogers L.L., Dowla F.U. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour. Res. 1994, 30(2):457-481.
42. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors. Nature 1986, 323(9):533-536.
43. Sahoo G.B., Ray C., De Carlo E.H. Use of neural network to predict flash flood and attendant water qualities of mountainous stream on Oahu, Hawaii. J. Hydrol. 2006, 327(3-4):525-538.
44. Sharma A. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1-A strategy for system predictor identification. J. Hydrol. 2000, 239(1-4):232-239.
45. Sharma A. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3-A nonparametric probabilistic forecast model. J. Hydrol. 2000, 239(1-4):249-258.
46. Sharma A., Luk K.C., Cordery I., Lall U. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 2-Predictor identification of quarterly rainfall using ocean-atmosphere information. J. Hydrol. 2000, 239(1-4):240-248.
47. Sudheer K.P., Gosain A.K., Ramasastri K.S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Process. 2002, 16(6):1325-1330.
48. Tamura S., Tateishi M. Capabilities of a four-layered feedforward neural network: Four layers versus three. IEEE Trans. Neural Networks 1997, 8(2):251-255.
49. Tayfur G., Singh V.P. ANN and fuzzy logic models for simulating event-based rainfall-runoff. J. Hydraul. Eng. 2006, 132(12):1321-1330.
50. Tokar A.S., Johnson P.A. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 1999, 4(3):232-239.
51. Weigend A.S., Huberman B.A., Rumelhart D.E. Predicting the future: a connectionist approach. Int. J. Neural Syst. 1990, 1(3):193-209.