Estimation of the Spatial Variation of Stream Flow by Neural Models and Surface Algorithms

  • Mrinmoy MajumderEmail author
  • Suchita Dutta
  • Rabindra Nath Barman
  • Pankaj Roy
  • Asis Mazumdar


The present study tried to estimate spatial variation of stream flow in face of climatic uncertainty. Seven neurogenetic models with different but related input variables were used to predict stream flow of both gauged and ungauged sub-basins of two river networks of Eastern India’s intratropical region. The models were validated and the better model was selected for estimation of stream flow. PARITYCGD was found to be the better model due to its lower RMSE and higher efficiency than any other considered models. The PARITYCGD model was then compared with three conceptual hydrologic models, and here also it was selected as the better model due to higher efficiency and reliability and lower RMSE and uncertainty than the other considered conceptual models. The radial basis surface interpolation was now used to generate spatial variation of stream flow within the two river networks due to the generated weather scenarios by PRECIS climate models. According to the results, the degradation of catchment, which is generally interpreted from high magnitude of stream flow, was observed in north-west and north-east region within the two river networks as per the surface diagram generated from model predictions due to observed rainfall and land-use data. For the future climatic data, spatial variation of stream flow was found to be concentrated in the same two regions only the areas showed an increasing trend, which was more in A2 scenario than in the case of B2 scenario of climate change. The model output was applied to generate spatial variation of water quality and pollution, which are explained in Chapters 10 and 11.


Distributed hydrology neural network stream flow surface algorithms 


  1. Ahmad M, Ghumman A, Ahmad S (2009) Estimation of clark’s instantaneous unit hydrograph parameters and development of direct surface runoff hydrograph. Water Resour Manage 23:2417–2435CrossRefGoogle Scholar
  2. Beatson RK, Cherrie JB, Mouat CT (1999) Fast fitting of radial basis functions: methods based on preconditioned GMRES iteration. Adv Comput Math 11(2–3):253–270CrossRefGoogle Scholar
  3. Bhatt VK, Bhattacharya P, Tiwari AK (2007) Application of artificial neural network in estimation of rainfall erosivity. Hydrol J 1–2(March–June):30–39Google Scholar
  4. Blazkova S, Beven KJ, Kulasova A (2002) On constraining topmodel hydrograph simulations using partial saturated area information. Hydrol Process 16(2):441–458CrossRefGoogle Scholar
  5. Brikowski RT (2007) GEOS, 5313, Lecture notes, Spring, UTD, MODRAT,
  6. Callahan TJ, Cook JD, Coleman MD, Amatya DM, Trettin CC (2004) Modeling storm water runoff and soil interflow in a managed forest, Upper coastal plain of the southeast US, Proceedings of ASAE annual meeting, American Society of Agricultural and Biological Engineers, Paper number 042254Google Scholar
  7. Carpenter TM, Georgakakos KP (2006) Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales. J Hydrol 329(1–2):174–185CrossRefGoogle Scholar
  8. Carr JC, Beatson RK, McCallum BC, Fright WR, McLennan TJ, Mitchell TJ (2003) ACM graphite 2003, Melbourne, Australia, pp 119–126Google Scholar
  9. Carr JC, Beatson RK, Cherrie JB, Mitchell TJ, Fright WR, McCallum BC, Evans TR (2001) ACM siggraph 2001, Los Angeles, CA, pp 67–76Google Scholar
  10. Carr JC, Fright WR, Beatson RK (1997) Surface interpolation with radial basis functions for medical imaging. IEEE Trans Med Imag 16(1):96–107CrossRefGoogle Scholar
  11. Clair TA, Ehrman JM (1998) Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resour Res 34(3):447–455CrossRefGoogle Scholar
  12. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3–4):244–257CrossRefGoogle Scholar
  13. Draper N, Smith H (1981) Applied regression analysis, 2nd edn. Wiley-Interscience, New York, p 709Google Scholar
  14. Dutta D, Herath S, Musiake K (1999) Distributed hydrologic model for flood inundation simulation, Proceedings of hydraulic engineering, JSCE, vol 43. Tokyo, Japan, pp 25–30Google Scholar
  15. El-Shafie A, Taha MR, Noureldin AA (2007) Neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manage 21(3):533–556CrossRefGoogle Scholar
  16. Elshorbagy A, Simonovic SP, Panu US (2000) Performance evaluation of artificial neural networks for runoff prediction. J Hydrol Eng 5(4):424–427CrossRefGoogle Scholar
  17. Eslami HR, Mohammadi K (2002) Application of ANN for reservoir inflow forecasting using snowmelt equivalent in the Karaj river watershed. Lowland Technol Int 4(2):17–26Google Scholar
  18. Fasshauer GE (2007) Meshfree approximation methods with MATLAB. World Scientific, SingaporeGoogle Scholar
  19. Fernando DA, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209CrossRefGoogle Scholar
  20. Franco C, Drew AP, Heisler G (2008) Impacts of urban runoff on native woody vegetation at Clark reservation state park, Jamesville, NY, J Urb Habit. Retrieved from on June 2009
  21. Gomi T, Sidle RC, Miyata S, Kosugi K, Onda Y (2008) Dynamic runoff connectivity of overland flow on steep forested hillslopes: Scale effects and runoff transfer. Water Resour Res 44:W08411. doi: 10.1029/2007WR005894 CrossRefGoogle Scholar
  22. GWSP Digital Water Atlas (2008) Map 52: change in runoff due to deforestation (V1.0). Available online at
  23. He W, Chen J, Dai H (2008) Application of decision support system to the Three Gorges Reservoir operation. J Hydroelectric Eng 27(2):11–16Google Scholar
  24. Hörmann G, Zhang X, Fohrer N (2007) Comparison of a simple and a spatially distributed hydrologic model for the simulation of a lowland catchment in Northern Germany, 209-1, pp 21–28Google Scholar
  25. Hsu K, Gupta HV, Sorooshian S (1995) Artifical neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–253CrossRefGoogle Scholar
  26. Idson PFF (2009) Methods of studying the dependence of river runoff on the forest coverage of its basin. Retrieved from on June 2009
  27. Imrie CE, Durucan S, Korre A (2000) River flow prediction using neural networks: generalization beyond the calibration range. J Hydrol 233(3–4):138–154CrossRefGoogle Scholar
  28. Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manage 125(5):263–271CrossRefGoogle Scholar
  29. Kim Y-O, Eum H-I, Lee E-G, Ko IH (2007) Optimizing operational policies of a Korean multireservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction. J Water Resour Plan Manage 133(1):4–14CrossRefGoogle Scholar
  30. Kisi O (2004) Multilayer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 1025–1040Google Scholar
  31. Kite G (2001) SLURP 12.7 hydrologic model. Water Resource Publications, (Accessed on 7 February 2009)
  32. Kojekine N, SavchenkoV, Ichiro H (2004) Geometric modeling: techniques, applications, systems and tools,, 218–231
  33. Lau CC, Lee KT, Tung CP, Chang CH (1999) Assessment of climate-change impact on runoff using normalized difference vegetation index. Retrieved from on June 2009
  34. Liong SY, Khu ST, Chan WT (2001) Derivation of Pareto front with genetic algorithm and neural network. J Hydrol Eng 6(1):52–61CrossRefGoogle Scholar
  35. Liepa P (2003) Filling holes in meshes. Proceedings of the 2003 eurographics/ACM. Retrieved from, pp 200–205Google Scholar
  36. Long ZQ, Zhang XH, Lin X-D, Chen ZJ (2007) Model for calculating benefit of forecasting intermediate inflow from downstream of reservoirs in water resources dispatch. J Hydraulic Eng 38(3):371–377Google Scholar
  37. Lopez JJ, Gimena FN, Goni M, Agirre U (2005) Analysis of a unit hydrograph model based on watershed geomorphology represented as a cascade of reservoirs. Agric Water Manage 77(1–3):128–143CrossRefGoogle Scholar
  38. Luo W, Weiss E (2002) Evaluation of standard error of forecast of runoff volume using linear regression models. Can J Civil Eng 29(5):635–640CrossRefGoogle Scholar
  39. Maidment DR (1993) Developing a spatially distributed unit hydrograph by using GIS. Proceedings of HydroGIS’93. IAHS Publ. no. 211Google Scholar
  40. Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting. Water Resour Res 35(8):2591–2596CrossRefGoogle Scholar
  41. Martens SN, Breshears DD (2005) Assessing contaminant transport vulnerability in complex topography using a distributed hydrologic model. Vadose Zone 4:811–818CrossRefGoogle Scholar
  42. Meselhe EA, Habib E, Oche OC, Gautam S (2004) World Water Congress 2004. Part of critical transitions in water and environmental resources management world water and environmental resources congressGoogle Scholar
  43. Nunes JC, Bouaoune Y, Delechelle E, Niang O, Bunel PH (2003) Image analysis by bidimensional empirical mode decomposition. Image Vision Comput 21(12):1019–1026CrossRefGoogle Scholar
  44. Oliviera FP (2006) Hydric erosion in forest areas in the Rio DoceValley, Central-East Region of the state of Minas Gerais, University Federal de Lavras, Brazil. Retrieved from on June, 2009
  45. Olivera F, Maidment D (1999) Geographic information systems (GIS)-based spatially distributed model for runoff routing. Water Resour Res 35(4):1135–1164CrossRefGoogle Scholar
  46. Saghafian B, Pierre JY, RAJAIE H (2002) Runoff hydrograph simulation based on time variable isochrone technique. J Hydrol 261(1–4):193–203CrossRefGoogle Scholar
  47. Shrestha MN (2003) MapAsia. Retrieved from on 7 February 2009
  48. Smith MB, Koren V, Reed SM, Zhang Z, Moreda F, Cui Z, Lei F, Cong SS (2004) The distributed hydrologic model intercomparison project phase 2 (DMIP 2): overview and initial NWS results,
  49. Statistics Solution (2009) Retrieved from on 16 July 2009
  50. Sui J (2005) Estimation of design flood hydrograph for an ungauged watershed. Water Resour Manage 19(6):813–830CrossRefGoogle Scholar
  51. Taskinen A, Bruen M (2007) Incremental distributed modelling investigation in a small agricultural catchment: 1. Overland flow with comparison with the unit hydrograph model. Hydrol Process 21(1):80–91CrossRefGoogle Scholar
  52. Tiju C, Xiaojing T (2007) Impact of forest harvesting on river runoff in the Xiaoxing’an Mountains of China. J Frontiers Forest China 2(2):143–147CrossRefGoogle Scholar
  53. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrologic Eng 4(3):232–239CrossRefGoogle Scholar
  54. Wei CC, Hsu NS (2007) Development of a real-time optimization model for flood control of a multipurpose reservoir. J Chinese Inst Civil Hydraulic Eng 19(3):355–365Google Scholar
  55. Wemple BC, Jones JA (2003) Runoff production on forest roads in a steep, mountain catchment. Water Resour Res 39(8):1220. doi: 10.1029/2002WR001744 CrossRefGoogle Scholar
  56. Xu ZX, Li JY (2001) Short-term inflow forecasting using an artificial neural network model. Wiley, West SussexGoogle Scholar
  57. Yeh K, Yang J, Tung Y (1997) Regionalization of unit hydrograph parameters: 2. Uncertainty analysis. Stochast Hydrol Hydr 11(2):173–192CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Mrinmoy Majumder
    • 1
    • 2
    Email author
  • Suchita Dutta
    • 2
  • Rabindra Nath Barman
    • 2
    • 3
  • Pankaj Roy
    • 1
  • Asis Mazumdar
    • 1
    • 2
  1. 1.School of Water Resources EngineeringJadavpur UniversityKolkataIndia
  2. 2.Regional Center, National Afforestation and Eco-development BoardJadavpur UniversityKolkataIndia
  3. 3.Department of ProductionNational Institute of TechnologyAgartalaIndia

Personalised recommendations