A threshold artificial neural network model for improving runoff prediction in a karst watershed

Environmental Earth Sciences

Volumn 74, Issue 6, 2015, Pages 5039-5048

  Meng, Xianmeng ^a   Yin, Maosheng ^a   Ning, Libo ^a   Liu, Dengfeng ^b   Xue, Xianwu ^c  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b XI'AN UNIVERSITY OF TECHNOLOGY   (China)

^c UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Artificial neural network; Karst watershed; Simulation; Threshold

Indexed keywords

AQUIFERS; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; COMPLEX NETWORKS; FLOODS; FORECASTING; NEURAL NETWORKS; RAIN; RUNOFF; STREAM FLOW; WATERSHEDS;

ARTIFICIAL NEURAL NETWORK MODELING; ARTIFICIAL NEURAL NETWORK MODELS; CORRELATIVE ANALYSIS; HYDROLOGICAL PREDICTION; NONLINEAR CHARACTERISTICS; RUNOFF GENERATION PROCESS; SIMULATION; THRESHOLD;

LANDFORMS;

AQUIFER; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COMPLEXITY; DISCHARGE; KARST; PRECIPITATION (CLIMATOLOGY); PREDICTION; RAINFALL-RUNOFF MODELING; STREAMFLOW; THRESHOLD; WATERSHED;

CHINA; HUBEI;

EID: 84940723980     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-015-4562-9     Document Type: Article

References (48)

1. Abrahart JR, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14:2157–2172
2. Arnold JG, Allen PM, Muttiah R, Bernhardt G (1995) Automated base flow separation and recession analysis techniques. Gr Water 33:1010–1018
3. ASCE (2000a) Task committee on application of artificial neural networks in hydrology, artificial neural networks in hydrology part I: preliminary concepts. J Hydrol Eng 5:115–123
4. ASCE (2000b) Task committee on application of artificial neural networks in hydrology, artificial neural networks in hydrology part II: hydrologic applications. J Hydrol Eng 5:124–137
5. Barrett ME, Charbeneau RJ (1997) A parsimonious model for simulating flow in a karst aquifer. J Hydrol 196:47–65
6. Bonacci O (2001) Analysis of the maximum discharge of karst springs. Hydrogeol J 9:328–338
7. Bras R, Rodriguez-Iturbe I (1985) Random functions and hydrology. Addison Wesley, Reading
8. Campbell CW, Sullivan SM (2002) Simulating time-varying cave flow and water levels using the storm water management model. Eng Geol 65:133–139
9. Chen JF, Jin QJ, Chao J (2012) Design of deep belief networks for short-term prediction of drought index using data in the Huaihe river basin. Math Probl Eng 2012:1–16
10. Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 27:185–195
11. Cigizoglu HK, Kisi O (2005) Flow prediction by three backpropagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36:1–16
12. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303–314
13. Dawson CW, Wilby RL (2001) Hydrological modeling using artificial neural networks. Prog Phys Geogr 25:80–108
14. Dreiss SJ (1982) Linear kernels for karst aquifers. Water Resour Res 18:865–876
15. Dreiss SJ (1983) Linear unit-response functions as indicators of recharge areas for large karst springs. J Hydrol 61:31–44
16. Eisenlohr L, Kiral L, Bouzelboudjen M, Rossier Y (1997) Numerical simulation as a tool for checking the interpretation of karst spring hydrographs. J Hydrol 193:306–315
17. Fan YH, Huo XL, Hao YH, Liu Y, Wang TK, Liu YC, Yeh TCJ (2013) An assembled extreme value statistical model of karst spring discharge. J Hydrol 504:57–68
18. Feisi Research and Development Center of Science and Technology (2003) MATAB 6.5 auxiliary neural network analysis and design. Electronics Industry Press, Beijing
19. Felton GK, Currens JC (1994) Peak flow rate and recession-curve characteristics of a karst spring in the inner bluegrass, central Kentucky. J Hydrol 162:119–141
20. Furundzic D (1998) Application example of neural networks for time series analysis: rainfall runoff modeling. Sig Process 64:383–396
21. Ge ZX, Sun ZQ (2007) Neural network theory and MATLAB R2007 realization. Electronic Industry Press, Beijing
22. Halihan T, Wicks CM (1998) Modeling of storm responses in conduit flow aquifers with reservoirs. J Hydrol 208:82–91
23. Hornik K, Stinchcombe M, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2:359–366
24. Hu HP, Tian FQ (2007) Advancement in research of physically based watershed hydrological model. J Hydraul Eng 38:511–517 (in Chinese)
25. Hu TS, Lam KC, Ng ST (2001) River flow time series prediction with a range-dependent neural network. Hydrol Sci J 46:729–745
26. Hu TS, Lam KC, Ng ST (2005) A modified neural network for improving river flow prediction. Hydrol Sci J 50(2):299–318
27. Hu CH, Hao YH, Yeh TC, Pang B, Wu ZN (2008) Simulation of spring flows from a karst aquifer with an artificial neural network. Hydrol Process 22:596–604
28. Jain A, Srinivasulu S (2004) Development of effective and efficient rainfall runoff models using integration of deterministic, real-coded GA, and ANN techniques. Water Resour Res 40:W043021–W0430212
29. Jiang LH, Wang AG, Tian NY, Zhang WC, Fan QL (2011) BP neural network of continuous casting technological parameters and secondary dendrite arm spacing of spring steel. J Iron Steel Res Int 18:25–29
30. Ju Q, Yu ZB, Hao ZC, Ou GX, Zhao J, Liu DD (2009) Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model. Neurocomputing 72:2873–2883
31. Jukic D, Denic-Jukic V (2006) Nonlinear kernel functions for karst aquifers. J Hydrol 328:360–374
32. Labat D, Ababou R, Mangin A (2000a) Rainfall-runoff relations for karstic springs, part I: convolution and spectral analyses. J Hydrol 238:123–148
33. Labat D, Ababou R, Mangin A (2000b) Rainfall-runoff relations for karstic springs, part II: continuous wavelet and discrete orthogonal multiresolution analyses. J Hydrol 238:149–178
34. Labat D, Ababou R, Mangin A (2001) Introduction of wavelet analyses to rainfall/runoffs relationship for a karstic basin: the case of Licq–Atherey karstic system (France). Gr Water 39:605–615
35. Lambrakis N, Andreou AS, Polydoropoulos P, Georgopoulos E, Bountis T (2000) Nonlinear analysis and forecasting of a brackish karstic spring. Water Resour Res 36:875–884
36. Liu YB, Batelaan O, De Smedt F, Huong NT, Tam VT (2005) Test of a distributed modelling approach to predict flood flows in the karst Suoimuoi catchment in Vietnam. Environ Geol 48:931–940
37. Makridakis S, Wheelwright SC, McGee VE (1983) Forecasting methods and applications, 2nd edn. Wiley, New York
38. Mangin A (1975a) Contribution a` l’e´tude hydrodynamique des aquife`res karstiques. Ann Spe´le´ologie 29(3):283–332
39. Mangin A (1975b) Contribution a` l’e´tude hydrodynamique des aquife`res karstiques. Ann Spe´le´ologie 29(4):495–601
40. Mangin A (1975c) Contribution a` l’e´tude hydrodynamique des aquife`res karstiques”. Ann Spe´le´ologie 30(1):21–124
41. Ministry of Water Resources of the People’s Republic of China (2000) Standard for hydrological information and hydrological forecasting, Beijing, China. Water Power Press, Beijing
42. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part I. A discussion of principles. J Hydrol 10:282–290
43. Padilla A, Pulido-Bosch A (2008) Simple procedure to simulate karstic aquifers. Hydrol Process 22:1876–1884
44. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel Distrib Process 1:318–362
45. Sun AY, Painter SL, Green RT (2005) Modeling Barton springs segment of the Edwards aquifer using MODFLOW-DCM. In: Proceedings of sinkholes and the engineering and environmental impacts of Karst, pp 163–177
46. Wang W, van Gelder PHAJM, Vrijling JK, Ma J (2006) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324:383–399
47. Zhang B, Govindaraju S (2000) Prediction of watershed runoff using bayesian concepts and modular neural networks. Water Resour Res 36:753–762
48. Zhang L, Subbarayan G (2002) An evaluation of back-propagation neural networks for the optimal design of structural systems: part I. Training procedures. Comput Methods Appl Mech Eng 191:2873–2886