Abstract
Regional flood frequency analysis (RFFA) is widely used in practice to estimate flood quantiles in ungauged catchments. Most commonly adopted RFFA methods such as quantile regression technique (QRT) assume a log-linear relationship between the dependent and a set of predictor variables. As non-linear models and universal approximators, artificial neural networks (ANN) have been widely adopted in rainfall runoff modeling and hydrologic forecasting, but there have been relatively few studies involving the application of ANN to RFFA for estimating flood quantiles in ungauged catchments. This paper thus focuses on the development and testing of an ANN-based RFFA model using an extensive Australian database consisting of 452 gauged catchments. Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT. Seven different regions have been compared with the ANN-based RFFA model and it has been shown that when the data from all the eastern Australian states are combined together to form a single region, the ANN presents the best performing RFFA model. This indicates that a relatively larger dataset is better suited for successful training and testing of the ANN-based RFFA models.
Similar content being viewed by others
References
Abrahart RJ, Kneale PE, See L (eds) (2004) Neural networks for hydrological modelling. Taylor & Francis, London
Abrahart RJ, Heppenstall AJ, See LM (2007) Timing error correction procedure applied to neural network rainfall-runoff modelling. Hydrol Sci J 52(3):414–431
Acreman MC, Sinclair CD (1986) Classification of drainage basins according to their physical characteristics and application for flood frequency analysis in Scotland. J Hydrol 84(3):365–380
ASCE Task Committee (2000) Artificial neural networks in hydrology-I: Preliminary concepts. J Hydrol Eng 5(2):115–123
Bates BC, Rahman A, Mein RG, Weinmann PE (1998) Climatic and physical factors that influence the homogeneity of regional floods in south-eastern Australia. Water Resour Res 34(12):3369–3382
Bayazit M, Onoz B (2004) Sampling variances of regional flood quantiles affected by inter-site correlation. J Hydrol 291:42–51
Benson MA (1962) Evolution of methods for evaluating the occurrence of floods. U.S. Geological Surveying Water Supply Paper, 1580-A, p 30
Besaw L, Rizzo DM, Bierman PR, Hackett WR (2010) Advances in ungauged streamflow prediction using artificial neural networks. J Hydrol 386(1–4):27–37
Blöschl G, Sivapalan M (1997) Process controls on regional flood frequency: coefficient of variation and basin scale. Water Resour Res 33:2967–2980
Bobee B, Cavadias G, Ashkar F, Bernier J, Rasmussen P (1993) Towards a systematic approach to comparison of distributions used in flood frequency analysis. J Hydrol 142:121–136
Burn DH (1990) Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour Res 26(10):2257–2265
Cavadias GS, Ouarda TBMJ, Bobee B, Girard C (2001) A canonical correlation approach to the determination of homogeneous regions for regional flood estimation of ungauged basins. Hydrol Sci J 46(4):499–512
Chokmani K, Ouarda BMJT, Hamilton S, Ghedira MH, Gingras H (2008) Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques. J Hydrol 349:83–396
Cunnane C (1988) Methods and merits of regional flood frequency analysis. J Hydrol 100:269–290
Dalrymple T (1960) Flood frequency analyses. U.S. Geological Survey Water Supply Paper 1543-A, pp 11–51
Daniell TM (1991) Neural networks: applications in hydrology and water resources engineering. International Hydrology & Water Resources Symposium. Perth, Australia, 2–4 Oct 1991
Dastorani MT, Wright NG (2001) Application of artificial neural networks for ungauged catchment flood prediction. Floodplain Management Association Conference, San Diego, CA
Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25(1):80–108
Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409
Farmer JD, Sidorowich J (1987) Predicting chaotic time series. Phys Rev Lett 59(8):845–848
Flavell D (2012) Design flood estimation in Western Australia. Aust J Water Resour 16(1):1–20
Gao C, Gemmer M, Zeng X, Liu B, Su B, Wen Y (2010) Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network. Stoch Environ Res Risk Assess 24:685–697
Govindaraju RS (2000) Artificial neural networks in hydrology II. Hydrological applications. J Hydrol Eng 5(2):124–137
Greis NP, Wood EF (1983) Regional flood frequency estimation and network design. Water Resour Res 17:1167–1174
Griffis VW, Stedinger JR (2007) The use of GLS regression in regional hydrologic analyses. J Hydrol 344:82–95
Grubbs FE, Beck G (1972) Extension of sample sizes and percentage points for significance tests of outlying observations. Technometrics 14:847–854
Guse B, Thieken AH, Castellarin A, Merz B (2010) Deriving probabilistic regional envelope curves with two pooling methods. J Hydrol 380(1–2):14–26
Haddad K, Rahman A (2011) Regional flood estimation in New South Wales Australia using generalised least squares quantile regression. J Hydrol Eng 16(11):20–925. doi:10.1061/(ASCE)HE.1943-5584.0000395
Haddad K, Rahman A (2012a) Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework: quantile regression vs. parameter regression technique. J Hydrol. doi:10.1016/j.jhydrol.2012.02.012
Haddad K, Rahman A (2012b) ‘Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework: quantile regression vs parameter regression technique. J Hydrol 20:142–161
Haddad K, Rahman A, Weinmann PE, Kuczera G, Ball JE (2010) Streamflow data preparation for regional flood frequency analysis: lessons from south-east Australia. Aust J Water Resour 14(1):17–32
Haddad K, Rahman A, Stedinger JR (2012) Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques. Hydrol Process 26:1008–1021
Hopfield J (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(8):2554–2558
Hosking JRM, Wallis JR (1993) Some statistics useful in regional frequency analysis. Water Resour Res 29(2):271–281
Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, New York
Huo Z, Feng S, Kang S, Huang G, Wang F, Guo P (2012) Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China. J Hydrol 420–421:159–170
Institution of Engineers Australia (I.E. Aust.) (1987/2001) Australian rainfall and runoff: a guide to flood estimation. In: Pilgrim DH (ed), vol 1. I. E. Aust, Canberra
Ishak E, Haddad K, Zaman M, Rahman A (2011) Scaling property of regional floods in New South Wales Australia. Nat Hazards 58:1155–1167. doi:10.1007/s11069-011-9719-6
Ishak E, Rahman A, Westra S, Sharma A, Kuczera G (2013) Evaluating the non-stationarity of Australian annual maximum floods. J Hydrol 494:134–145
Javelle P, Ouarda BMJT, Lang M, Bobee B, Galea G, Gresillon JM (2002) Development of regional flood-duration-frequency curves based on the index flood method. J Hydrol 258:249–259
Jiapeng H, Zhongmin L, Zhongbo Y (2003) A modified rational formula for flood design in small basins. J Am Water Resour Assoc 39(5):1017–1025
Jingyi Z, Hall MJ (2004) Regional flood frequency analysis for the Gan-Ming River basin in China. J Hydrol 296:98–117
Kashani MH, Montaseri M, Yaghin MAL (2008) Flood estimation at ungauged sites using a new hybrid model. J Appl Sci 8:1744–1749
Kendall MG (1970) Rank correlation methods, 2nd edn. Hafner, New York
Kirby W, Moss M (1987) Summary of flood frequency analysis in the United States. J Hydrol 96:5–14
Kjeldsen TR, Jones D (2009) An exploratory analysis of error components in hydrological regression modelling. Water Resour Res 45:W02407. doi:10.1029/2007WR006283
Kjeldsen TR, Jones DA (2010) Predicting the index flood in ungauged UK catchments: on the link between data-transfer and spatial model error structure. J Hydrol 387(1–2):1–9. doi:10.1016/j.jhydrol.2010.03.024
Kothyari UC (2004) Estimation of mean annual flood from ungauged catchments using artificial neural networks. In: Proceedings of the British hydrological society international conference on hydrology: science and practice for the 21st century, vol. 1
Kuczera G (1999) Comprehensive at-site flood frequency analysis using Monte Carlo Bayesian inference. Water Resour Res 35(5):1551–1557
Luk KC, Ball JE, Sharma A (2001) An application of artificial neural networks for rainfall forecasting. Math Comput Model 33:683–693
Madsen H, Pearson CP, Rosbjerg D (1997) Comparison of annual maximum series and partial duration series for modeling extreme hydrological events—2. Regional modeling. Water Resour Res 33(4):771–781
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(1):101–123
McCulloch WS, Pitts W (1943) A logic calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133
Mulvany TJ (1851) On the use of self-registering rain and flood gauges. Inst Civ Eng Trans (Ireland) 4(2):1–8
Muttiah RS, Srinivasan R, Allen PM (1997) Prediction of two year peak stream discharges using neural networks. J Am Water Resour Assoc 33(3):625–630
Nathan RJ, McMahon TA (1990) Identification of homogeneous regions for the purpose of regionalisation. J Hydrol 121:217–238
National Research Council (NRC) (1988) Estimating probabilities of extreme floods: methods and recommended research. National Academy Press, Washington, DC, p 141
NERC (1975) Flood studies report, vol. 5. Natural Environment Research Centre (NERC), London
Ouarda TBMJ, Bâ KM, Diaz-Delgado C, Cârsteanu C, Chokmani K, Gingras H, Quentin E, Trujillo E, Bobée B (2008) Intercomparison of regional flood frequency estimation methods at ungauged sites for a Mexican case study. J Hydrol 348:40–58
Pallard B, Castellarin A, Montanari A (2009) A look at the links between drainage density and flood statistics. Hydrol Earth Syst Sci (HESS) 13:1019–1029
Pandey GR, Nguyen VTV (1999) A comparative study of regression based methods in regional flood frequency analysis. J Hydrol 225:92–101
Pegram GGS, Parak M (2004) A review of the regional maximum flood and rational formula using geomorphological information and observed floods. Water S Afr 30(3):377–392
Potter KW (1987) Research on flood frequency analysis, 1983–1986. Rev Geophys 25(2):113–118
Rahman A (2005) A quantile regression technique to estimate design floods for ungauged catchments in south-east Australia. Aust J Water Resour 9(1):81–89
Rahman A, Bates BC, Mein RG, Weinmann PE (1999) Regional flood frequency analysis for ungauged basins in south-eastern Australia. Aust J Water Resour 3(2):199–207
Rahman A, Haddad K, Zaman M, Kuczera G, Weinmann PE (2011) Design flood estimation in ungauged catchments: a comparison between the probabilistic rational method and quantile regression technique for NSW. Aust J Water Resour 14(2):127–137
Rao AR, Srinivas VV (2008) Regionalization of watersheds: an approach based on cluster analysis. Springer, Berlin, ISBN: 14020685142008
Riad S, Mania J, Bouchaou L, Najjar Y (2004) Rainfall-runoff model using an artificial neural network approach. Math Comput Model 40(7–8):839–846
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In Rumelhart DE, McClelland JL, PDP Research Group (eds) Paralled distributed processing. Explorations in the microstructure of cognition, vol. 1. MIT Press, Cambridge, MA, pp 318–362
Shamseldin AY (1997) Application of a neural network technique to rainfall-runoff modelling. J Hydrol 199:272–294
Shu C, Burn DH (2004) Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resour Res 40(9):W09301. doi:10.1029/2003WR002816
Shu C, Ouarda TBMJ (2007) Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resour Res 43:W07438. doi:10.1029/2006WR005142
Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43
Stedinger JR, Tasker GD (1985) Regional hydrologic analysis: 1. Ordinary, weighted and generalized least squares compared. Water Resour Res 21:1421–1432
Tasker GD (1980) Hydrologic regression with weighted least squares. Water Resour Res 16(6):1107–1113
Tasker GD, Eychaner JH, Stedinger JR (1986) Application of generalised least squares in regional hydrologic regression analysis. US Geological Survey Water Supply Paper 2310, pp 107–115
Thomas DM, Benson MA (1970) Generalization of streamflow characteristics from drainage-basin characteristics. U.S. Geological Survey Water Supply Paper 1975, US Governmental Printing Office
Turan ME, Yurdusev MA (2009) River flow estimation from upstream flow records by artificial intelligence methods. J Hydrol 369:71–77
World Meteorological Organization (WMO) (1989) Statistical distributions for flood frequency analysis. Operational Hydrology Report, 33
Wu J, Li N, Yang H, Li C (2008) Risk evaluation of heavy snow disasters using BP artificial neural network: the case of Xilingol in Inner Mongolia. Stoch Environ Res Risk Assess 22:719–725
Zaman M, Rahman A, Haddad K (2012) Regional flood frequency analysis in arid regions: a case study for Australia. J Hydrol 475:74–83
Zhang B, Govindaraju RS (2003) Geomorphology-based artificial neural networks for estimation of direct runoff over watersheds. J Hydrol 273(1):18–34
Zrinji Z, Burn DH (1994) Flood frequency analysis for ungauged sites using a region of influence approach. J Hydrol 153(1–4):1–21
Acknowledgments
The authors would like to acknowledge the financial supports of Geoscience Australia and Engineers Australia and various government and private organizations in Australia that provided the data for the project: Department of Sustainability and Environment (VIC), Australian Bureau of Meteorology, Department of Natural Resources and Water (QLD), Department of Water and Energy (NSW) and ENTURA (TAS). The authors would also like to thank two anonymous reviewers and the Associate Editor for very useful comments which have helped to improve the paper notably.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Aziz, K., Rahman, A., Fang, G. et al. Application of artificial neural networks in regional flood frequency analysis: a case study for Australia. Stoch Environ Res Risk Assess 28, 541–554 (2014). https://doi.org/10.1007/s00477-013-0771-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-013-0771-5