Skip to main content
SpringerLink
Log in

Spatial Information-Based Back-Propagation Neural Network Modeling for Outflow Estimation of Ungauged Catchment

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Rainfall–Runoff Models (RRMs) are standard tools for hydrologists to organize water resource planning and management. With the development of space-information technology and computing science in recent years, RRMs have improved to better simulate the rainfall–runoff process along with the spatial variation of simulated catchment, with the aim of precisely articulating the underlying relationship between input and output information. The Artificial Neural Network (ANN) model is suitable because it has various mathematical compositions capable of simulating a nonlinear structural system to establish the flow discharge in a catchment. For this reason, it has been successfully applied in RRMs modeling. In this study, the rainfall–runoff behavior of the gauge stations will be replicated in an ANN model. A Back-Propagation Neural network (BPN) was adopted to estimate an ungauged region’s outflow considering the temporal distribution of rainfall–runoff and the spatial distribution of the watershed environment. The nonlinear relationship among the physiographic factors, precipitation and outflow of the specific catchment was established to estimate the outflow of the sub-catchment where no flow gauge had been provided. The hazard preventive hourly model was mainly considered in this paper, and the model was also compared with the Instantaneous Unit Hydrograph (IUH) and Area Ratio Method (ARM) approaches. In validation tests in the middle and large catchments in Taiwan, the model performed well in providing forecast results. From the research work, it is revealed that the relevant spatial information can be obtained easily and precisely, which will help future studies on more related dimensions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Ahmad M, Ghumman A, Ahmad S (2009) Estimation of Clark’s instantaneous unit hydrograph parameters and development of direct surface runoff hydrograph. Water Resour Manag 23:2417–2435

    Article  Google Scholar 

  • Al-Qudah K, Abu-Jaber N (2009) A GIS database for sustainable management of shallow water resources in the Tulul al Ashaqif Region, NE Jordan. Water Resour Manag 23:603–615

    Article  Google Scholar 

  • Bowden GJ, Maier HR, Dandy GC (2002) Optimal division of data for neural network models in water resources applications. Water Resour Res 38:2-1–2-11. doi:10.1029/2001WR000266

    Article  Google Scholar 

  • Chan SC (2003) Flood estimation using artificial neural network based on physiographic features of watersheds. Ph.D., National Taiwan University

  • Cigizoglu HK, Kişi Ö (2005) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36:49–64

    Google Scholar 

  • Corani G, Guariso G (2005) An application of pruning in the design of neural networks for real time flood forecasting. Neural Comput Appl 14:66–77. doi:10.1007/s00521-004-0450-z

    Article  Google Scholar 

  • Cutore P, Cristaudo G, Campisano A, Modica C, Cancelliere A, Rossi G (2007) Regional models for the estimation of streamflow series in ungauged basins. Water Resour Manag 21:789–800

    Article  Google Scholar 

  • Dooge J, O’Kane JP (2003) Deterministic methods in systems hydrology. Balkema, Lisse

    Google Scholar 

  • Ellouze M, Abida H (2008) Regional flood frequency analysis in Tunisia: identification of regional distributions. Water Resour Manag 22:943–957

    Article  Google Scholar 

  • Engeland K, Hisdal H (2009) A comparison of low flow estimates in ungauged catchments using regional regression and the HBV-model. Water Resour Manag 23:2567–2586

    Article  Google Scholar 

  • Franchini M, Suppo M (1996) Regional analysis of flow duration curves for a limestone region. Water Resour Manag 10:199–218. doi:10.1007/BF00424203

    Article  Google Scholar 

  • Gupta VK, Waymire E, Wang CT (1980) A representation of an instantaneous unit hydrograph from geomorphology. Water Resour Res 16:855–862. doi:10.1029/WR016i005p00855

    Article  Google Scholar 

  • Ham FM (2001) Principles of neurocomputing for science and engineering. McGraw Hill, New York

    Google Scholar 

  • Haykin SS (1999) Neural networks. Prentice Hall, Hamilton

    Google Scholar 

  • Horton RE (1945) Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Geol Soc Amer Bull 56:275–370. doi:10.1130/0016-7606(1945)56[275:edosat]2.0.co;2

    Article  Google Scholar 

  • IAHS (2002) IAHS Decade of the prediction of ungauged basins. IAHS Newsletter 74

  • Jain SK, Singh RD, Seth SM (2000) Design flood estimation using GIS supported GIUHApproach. Water Resour Manag 14:369–376

    Article  Google Scholar 

  • Johnson DE (1998) Applied multivariate methods for data analysis. Duxbury, Pacific Grove

    Google Scholar 

  • Kuo YM, Liu CW (2000) Analysis on variation of groundwater quality in Yun-Lin coastal area: (I) Multivariate factor analysis method. Journal of Taiwan Water Conservancy 48:1–8

    Google Scholar 

  • LeBoutillier DW, Waylen PR (1993) A stochastic model of flow duration curves. Water Resour Res 29:3535–3541. doi:10.1029/93WR01409

    Article  Google Scholar 

  • Lin MW (2006) Time series analysis and forecasting, 3rd edn. Hwatai, Taipei

    Google Scholar 

  • Liu L-M, Hanssens DM (1982) Identification of multiple-input transfer function models. Commun Stat, Theory Methods 11:297–314

    Article  Google Scholar 

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124

    Article  Google Scholar 

  • Mimikou M, Kaemaki S (1985) Regionalization of flow duration characteristics. J Hydrol 82:77–91

    Article  Google Scholar 

  • Noto L, La Loggia G (2009) Use of L-moments approach for regional flood frequency analysis in Sicily, Italy. Water Resour Manag 23:2207–2229

    Article  Google Scholar 

  • Patil J, Sarangi A, Singh O, Singh A, Ahmad T (2008) Development of a GIS Interface for estimation of runoff from watersheds. Water Resour Manag 22:1221–1239

    Article  Google Scholar 

  • Ponce VM (1989) Engineering hydrology: principles and practices. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Quimpo RG, Alejandrino AA, McNally TA (1983) Regionalized flow duration for philippines. J Water Resour Plan Manage 109:320–330. doi:10.1061/(ASCE)0733-9496(1983)109:4(320)

    Article  Google Scholar 

  • Rodríguez-Iturbe I, Valdés JB (1979) The geomorphologic structure of hydrologic response. Water Resour Res 15:1409–1420. doi:10.1029/WR015i006p01409

    Article  Google Scholar 

  • Sanborn SC, Bledsoe BP (2006) Predicting streamflow regime metrics for ungauged streamsin Colorado, Washington, and Oregon. J Hydrol 325:241–261. doi:10.1016/j.jhydrol.2005.10.018

    Article  Google Scholar 

  • Sarangi A, Madramootoo C, Enright P, Prasher S (2007) Evaluation of three unit hydrograph models to predict the surface runoff from a Canadian watershed. Water Resour Manag 21:1127–1143

    Article  Google Scholar 

  • Sefton CEM, Howarth SM (1998) Relationships between dynamic response characteristics and physical descriptors of catchments in England and Wales. J Hydrol 211:1–16. doi:10.1016/S0022-1694(98)00163-2

    Article  Google Scholar 

  • Simon H (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey

    Google Scholar 

  • Spearman C (1904) General intelligence, objectively determined and measured. Am J Psychol 15:201–293

    Article  Google Scholar 

  • Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Geol Soc Amer Bull 63:1117–1142. doi:10.1130/0016-7606(1952)63[1117:haaoet]2.0.co;2

    Article  Google Scholar 

  • Venkata R, Eldho T, Rao E, Chithra N (2008) A distributed kinematic Wave–Philip infiltration watershed model using FEM, GIS and remotely sensed data. Water Resour Manag 22:737–755

    Article  Google Scholar 

  • Wagener T, Wheater H, Gupta HV (2004) Rainfall–runoff modelling in gauged and ungauged catchments. Imperial College Press, London

    Book  Google Scholar 

  • Wei WWS (2006) Time series analysis: univariate and multivariate methods. Addison Wesley, Redwood City

    Google Scholar 

  • Wilcock DN, Hanna JE, Essery CI (1987) Derivation of flow duration curves in Northern Ireland. In: Proceedings of the Institute of Civil Engineers Part 2, pp 381–396

  • Woods R (2002) Seeing catchments with new eyes. Spatial patterns in catchment hydrology: observations and modelling. In: Grayson R, Blöschl G (eds) Hydrol Process, vol 16, pp 1111–1113. doi:10.1002/hyp.539

  • Yu PS, Yang TC (1996) Synthetic regional flow duration curve for southern Taiwan. Hydrol Process 10:373–391. doi:10.1002/(SICI)1099-1085(199603)10:3<373::AID-HYP306>3.0.CO;2-4

    Article  Google Scholar 

  • Zhu ML, Fujita M (1994) Comparisons between fuzzy reasoning and neural network methods to forecast runoff discharge. J Hydrosci Hydraul Eng 12:131–141

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Water Resources Engineering and Conservation, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, Taichung, 40724, Taiwan, Republic of China

    Chang-Shian Chen

  2. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, No. 1, University Rd, Tainan, 70101, Taiwan, Republic of China

    Frederick Nai-Fang Chou & Boris Po-Tsang Chen

Authors
  1. Chang-Shian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frederick Nai-Fang Chou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Boris Po-Tsang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Boris Po-Tsang Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, CS., Chou, F.NF. & Chen, B.PT. Spatial Information-Based Back-Propagation Neural Network Modeling for Outflow Estimation of Ungauged Catchment. Water Resour Manage 24, 4175–4197 (2010). https://doi.org/10.1007/s11269-010-9652-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-010-9652-6

Keywords

Search

Navigation